Equivalence testing is of emerging importance in genomics studies but has
hitherto been little studied in this content. In this paper, we define the
notion of equivalence of gene expression and determine a `strength of evidence'
measure for gene equivalence. It is common practice in genome-wide studies to
rank genes according to observed gene-specific P-values or adjusted P-values,
which are assumed to measure the strength of evidence against the null
hypothesis of no differential gene expression.

We introduce the optimal obstacle placement with disambiguations problem
wherein the goal is to place true obstacles in an environment cluttered with
false obstacles so as to maximize the total traversal length of a navigating
agent (NAVA). Prior to the traversal, NAVA is given location information and
probabilistic estimates of each disk-shaped hindrance (hereinafter referred to
as disk) being a true obstacle. The NAVA can disambiguate a disk's status only
when situated on its boundary. There exists an obstacle placing agent (OPA)
that locates obstacles prior to NAVA's traversal.

Array comparative genomic hybridization(CGH) is a high resolution technique
to assess DNA copy number variation. Identifying breakpoints where copy number
changes will enhance the understanding of the pathogenesis of human diseases,
such as cancers. However, the biological variation and experimental errors
contained in array CGH data may lead to false positive identification of
breakpoints.

Familial Searching is the process of searching in a DNA database for
relatives of a given individual. It is well known that in order to evaluate the
genetic evidence in favour of a certain given form of relatedness between two
individuals, one needs to calculate the appropriate likelihood ratio, which is
in this context called a Kinship Index. Suppose that the database contains, for
a given type of relative, at most one related individual.

The optimal and robust design of structures has gained much attention in the
past ten years due to the ever increasing need for manufacturers to build
robust systems at the lowest cost. Reliability-based design optimization (RBDO)
allows the analyst to minimize some cost function while ensuring some minimal
performances cast as admissible probabilities of failure for a set of
performance functions. In order to address real-world problems in which the
performance is assessed through computational models (e.g.

Sparsity in the eigenvectors of signal covariance matrices is exploited in
this paper for compression and denoising. Dimensionality reduction (DR) and
quantization modules present in many practical compression schemes such as
transform codecs, are designed to capitalize on this form of sparsity and
achieve improved reconstruction performance compared to existing
sparsity-agnostic codecs.

We present a new model for the electricity spot price dynamics, which is able
to capture seasonality, low-frequency dynamics and the extreme spikes in the
market. Instead of the usual purely deterministic trend we introduce a
non-stationary independent increments process for the low-frequency dynamics,
and model the large fluctuations by a non-Gaussian stable CARMA process. The
model allows for analytic futures prices, and we apply these to model and
estimate the whole market consistently.

Many scholars have recently begun to dispute the assumed link between
individual wellbeing and economic conditions and the extent to which the latter
matters (Easterlin, 1995; Stevenson and Wolfers 2008; Tella and MacCulloch
2008). This dilemma is empirically demonstrated in the Latin America Public
Opinion Project (LAPOP, 2011), which surveyed North and Latin America in terms
of perceived life satisfaction. Higher measures found in the less developed
countries of Brazil, Costa Rica, and Panama than in North America pose an
intriguing quandary to traditional economic theory.

Mixtures of linear mixed models (MLMMs) are useful for clustering grouped
data in applications such as gene expression time course experiments. These
models can be estimated by likelihood maximization through the EM algorithm and
the optimal number of components determined by comparing different mixture
models using penalized log-likelihood criteria such as BIC. In this paper, we
propose fitting MLMMs with variational methods which can perform parameter
estimation and model selection simultaneously.

Joinpoint regression is used to determine the number of segments needed to
adequately explain the relationship between two variables. This methodology can
be widely applied to real problems, but we focus on epidemiological data, the
main goal being to uncover changes in the mortality time trend of a specific
disease under study. Traditionally, Joinpoint regression problems have paid
little or no attention to the quantification of uncertainty in the estimation
of the number of change-points. In this context, we found a satisfactory way to
handle the problem in the Bayesian methodology.

This paper proposes confidence regions for the identified set in conditional
moment inequality models using Kolmogorov-Smirnov statistics with a truncated
inverse variance weighting with increasing truncation points. The new weighting
differs from those proposed in the literature in two important ways. First,
confidence regions based on KS tests with the weighting function I propose
converge to the identified set at a faster rate than existing procedures based
on bounded weight functions in a broad class of models.

Network inference approaches are now widely used in biological applications
to probe regulatory relationships between molecular components such as genes or
proteins. Many methods have been proposed for this setting, but the connections
and differences between their statistical formulations have received less
attention. In this paper, we show how a broad class of statistical network
inference methods, including a number of existing approaches, can be described
in terms of variable selection for the linear model.

We propose a semiparametric model for autonomous nonlinear dynamical systems
and devise an estimation procedure for model fitting. This model incorporates
subject-specific effects and can be viewed as a nonlinear semiparametric mixed
effects model. We also propose a computationally efficient model selection
procedure. We show by simulation studies that the proposed estimation as well
as model selection procedures can efficiently handle sparse and noisy
measurements.

Monte Carlo approaches have recently been proposed to quantify connectivity
in neuronal networks. The key problem is to sample from the conditional
distribution of a single neuronal spike train, given the activity of the other
neurons in the network. Dependencies between neurons are usually relatively
weak; however, temporal dependencies within the spike train of a single neuron
are typically strong. In this paper we develop several specialized
Metropolis--Hastings samplers which take advantage of this dependency
structure.

Research in examining the equity of service accessibility has emerged as
economic and social equity advocates recognized that where people live
influences their opportunities for economic development, access to quality
health care and political participation. In this research paper service
accessibility equity is concerned with where and when services have been and
are accessed by different groups of people, identified by location or
underlying socioeconomic variables.

Covariance matrix estimates are an essential part of many signal processing
algorithms, and are often used to determine a low-dimensional principal
subspace via their spectral decomposition. However, exact eigenanalysis is
computationally intractable for sufficiently high-dimensional matrices, and in
the case of small sample sizes, sample eigenvalues and eigenvectors are known
to be poor estimators of their true counterparts. To address these issues, we
propose a covariance estimator that is computationally efficient while also
performing shrinkage on the sample eigenvalues.

Comonotonicity had been a extreme case of dependency between random
variables. This article consider an extension of single life model under
multiple dependent decrement causes to the case of comonotonic group-life.

Interactions among multiple genes across the genome may contribute to the
risks of many complex human diseases. Whole-genome single nucleotide
polymorphisms (SNPs) data collected for many thousands of SNP markers from
thousands of individuals under the case--control design promise to shed light
on our understanding of such interactions. However, nearby SNPs are highly
correlated due to linkage disequilibrium (LD) and the number of possible
interactions is too large for exhaustive evaluation.

Biomedical studies have a common interest in assessing relationships between
multiple related health outcomes and high-dimensional predictors. For example,
in reproductive epidemiology, one may collect pregnancy outcomes such as length
of gestation and birth weight and predictors such as single nucleotide
polymorphisms in multiple candidate genes and environmental exposures. In such
settings, there is a need for simple yet flexible methods for selecting true
predictors of adverse health responses from a high-dimensional set of candidate
predictors.

Genetic association study is an essential step to discover genetic factors
that are associated with a complex trait of interest. In this paper we present
a novel generalized quasi-likelihood score (GQLS) test that is suitable for a
study with either a quantitative trait or a binary trait. We use a logistic
regression model to link the phenotypic value of the trait to the distribution
of allelic frequencies. In our model, the allele frequencies are treated as a
response and the trait is treated as a covariate that allows us to leave the
distribution of the trait values unspecified.

The vast amount of biological knowledge accumulated over the years has
allowed researchers to identify various biochemical interactions and define
different families of pathways. There is an increased interest in identifying
pathways and pathway elements involved in particular biological processes. Drug
discovery efforts, for example, are focused on identifying biomarkers as well
as pathways related to a disease. We propose a Bayesian model that addresses
this question by incorporating information on pathways and gene networks in the
analysis of DNA microarray data.

In this paper we introduce a novel method to conduct inference with models
defined through a continuous-time Markov process, and we apply these results to
a classical stochastic SIR model as a case study. Using the inverse-size
expansion of van Kampen we obtain approximations for first and second moments
for the state variables. These approximate moments are in turn matched to the
moments of an inputed generic discrete distribution aimed at generating an
approximate likelihood that is valid both for low count or high count data.

In this paper, we consider capture-recapture experiments with heterogenous
catchability. In the setting we consider, the widespread Huggins-Alho estimator
is not very suitable and we introduce and study a new generalized
Horvitz-Thompson estimator. Our motivation is Respondent Driven Sampling (RDS),
a prime example for such a setting where the capture probability is dependent
on both the unknown population size as well as on an observable covariate, the
network degree of an individual, due to peer recruitment.

We consider the problem of algorithmically recommending items to users on a
Yahoo! front page module. Our approach is based on a novel multilevel
hierarchical model that we refer to as a User Profile Model with Graphical
Lasso (UPG). The UPG provides a personalized recommendation to users by
simultaneously incorporating both user covariates and historical user
interactions with items in a model based way. In fact, we build a per-item
regression model based on a rich set of user covariates and estimate individual
user affinity to items by introducing a latent random vector for each user.

This paper proposes a model of financial contagion that accounts for
explosive, mutually exciting shocks to market volatility. We fit the model
using country-level data during the European sovereign debt crisis, which has
its roots in the period 2008--2010, and was continuing to affect global markets
as of October, 2011.

In this paper, we study the target tracking problem in wireless sensor
networks (WSNs) using quantized sensor measurements under limited bandwidth
availability. At each time step of tracking, the available bandwidth $R$ needs
to be distributed among the $N$ sensors in the WSN for the next time step. The
optimal solution for the bandwidth allocation problem can be obtained by using
a combinatorial search which may become computationally prohibitive for large
$N$ and $R$.

Ambient concentrations of many pollutants are associated with emissions due
to human activity, such as road transport and other combustion sources. In this
paper we consider air pollution as a multi--level phenomenon within a Bayesian
hierarchical model. We examine different scales of variation in pollution
concentrations ranging from large scale transboundary effects to more localised
effects which are directly related to human activity.

Demand functions for goods are generally cyclical in nature with
characteristics such as trend or stochasticity. Most existing demand
forecasting techniques in literature are designed to manage and forecast this
type of demand functions. However, if the demand function is lumpy in nature,
then the general demand forecasting techniques may fail given the unusual
characteristics of the function.

We present a consensus-based distributed particle filter (PF) for wireless
sensor networks. Each sensor runs a local PF to compute a global state estimate
that takes into account the measurements of all sensors. The local PFs use the
joint (all-sensors) likelihood function, which is calculated in a distributed
way by a novel generalization of the likelihood consensus scheme. A performance
improvement (or a reduction of the required number of particles) is achieved by
a novel distributed, consensus-based method for adapting the proposal densities
of the local PFs.

Presented is an evolutionary model of consumer non-durable markets, which is
an extension of a previously published paper on consumer durables. The model
suggests that the repurchase process is governed by preferential growth.
Applying statistical methods it can be shown that in a competitive market the
mean price declines according to an exponential law towards a natural price,
while the corresponding price distribution is approximately given by a Laplace
distribution for independent price decisions of the manufacturers.

Association networks represent systems of interacting elements, where a link
between two different elements indicates a sufficient level of similarity
between element attributes. While in reality relational ties between elements
can be expected to be based on similarity across multiple attributes, the vast
majority of work to date on association networks involves ties defined with
respect to only a single attribute.

In this paper, we study a non-linear model used to estimate and forecast the
electricity load, that usually requires four or more years worth of data to
avoid any overfitting phenomenon. We first propose a non-informative prior to
be used when the number of observations is large enough. When the observations
are too few, we propose a hierarchical prior to include information coming from
another bigger, similar, sample. The posterior densities associated with these
two priors are derived and a MCMC algorithm is provided in each case.

We consider pricing weather derivatives for use as protection against weather
extremes. The method described utilizes results from spatial statistics and
extreme value theory to first model extremes in the weather as a max-stable
process, and then use these models to simulate payments for a general
collection of weather derivatives. These simulations capture the spatial
dependence of payments. Incorporating results from catastrophe ratemaking, we
show how this method can be used to compute risk loads and premiums for weather
derivatives which are renewal-additive.

Gene regulatory networks are collections of genes that interact with one
other and with other substances in the cell. By measuring gene expression over
time using high-throughput technologies, it may be possible to reverse
engineer, or infer, the structure of the gene network involved in a particular
cellular process.

In radar systems, tracking targets in low signal-to-noise ratio (SNR)
environments is a very important task. There are some algorithms designed for
multitarget tracking. Their performances, however, are not satisfactory in low
SNR environments. Track-before-detect (TBD) algorithms have been developed as a
class of improved methods for tracking in low SNR environments. However,
multitarget TBD is still an open issue. In this paper, multitarget TBD
measurements are modeled, and a highly efficient filter in the framework of
finite set statistics (FISST) is designed.

This paper constructs dynamical models and estimation algorithms for the
concentration of target molecules in a fluid flow using an array of novel
biosensors. Each biosensor is constructed out of protein molecules embedded in
a synthetic cell membrane. The concentration evolves according to an
advection-diffusion partial differential equation which is coupled with
chemical reaction equations on the biosensor surface.

Covariance is used as an inner product on a formal vector space built on n
random variables to define measures of correlation Md across a set of vectors
in a d-dimensional space. For d = 1, one has the diameter; for d = 2, one has
an area. These concepts are directly applied to correlation studies in climate
science.

Chaos and oscillations continue to capture the interest of both the
scientific and public domains. Yet despite the importance of these qualitative
features, most attempts at constructing mathematical models of such phenomena
have taken an indirect, quantitative approach, e.g. by fitting models to a
finite number of data-points. Here we develop a qualitative inference framework
that allows us to both reverse engineer and design systems exhibiting these and
other dynamical behaviours by directly specifying the desired characteristics
of the underlying dynamical attractor.

We develop a new statistical method for estimating functional connectivity
between neurophysiological signals represented by a multivariate time series.
We use partial coherence as the measure of functional connectivity. Partial
coherence identifies the frequency bands that drive the direct linear
association between any pair of channels. To estimate partial coherence, one
would first need an estimate of the spectral density matrix of the multivariate
time series.

Given a set of aligned sequences of independent noisy observations, we are
concerned with detecting intervals where the mean values of the observations
change simultaneously in a subset of the sequences. The intervals of changed
means are typically short relative to the length of the sequences, the subset
where the change occurs, the "carriers," can be relatively small, and the sizes
of the changes can vary from one sequence to another. This problem is motivated
by the scientific problem of detecting inherited copy number variants in
aligned DNA samples.

In this paper we bring to bear some new tools from statistical learning on
the analysis of roll call data. We present a new data-driven model for roll
call voting that is geometric in nature. We construct the model by adapting the
"Partition Decoupling Method," an unsupervised learning technique originally
developed for the analysis of families of time series, to produce a multiscale
geometric description of a weighted network associated to a set of roll call
votes.

Unbiased, label-free proteomics is becoming a powerful technique for
measuring protein expression in almost any biological sample. The output of
these measurements after preprocessing are a collection of features (10's to
100's of thousands) and their associated intensities for each sample. Subsets
of features within the data are from the same peptide, subsets of peptides are
from the same protein, and subsets of proteins are in the same biological
pathways, therefore there is the potential for very complex and informative
correlational structure inherent in this data.

Spatial Independent Component Analysis (ICA) decomposes the time by space
functional MRI (fMRI) matrix into a set of 1-D basis time courses and their
associated 3-D spatial maps that are optimized for mutual independence. When
applied to resting state fMRI (rsfMRI), ICA produces several spatial
independent components (ICs) that seem to have biological relevance - the
so-called resting state networks (RSNs). The ICA problem is well posed when the
true data generating process follows a linear mixture of ICs model in terms of
the identifiability of the mixing matrix.

Confounding of three binary-variables counterfactual model is discussed in
this paper. According to the effect between the control variable and the
covariate variable, we investigate three counterfactual models: the control
variable is independent of the covariate variable, the control variable has the
effect on the covariate variable and the covariate variable affects the control
variable.

Variance estimation for estimators of state, county, and school district
quantities derived from the Census 2000 long form are discussed. The variance
estimator must account for (1) uncertainty due to imputation, and (2) raking to
census population controls.

We study the causal effect of winning an Oscar Award on an actor or actress's
survival. Does the increase in social rank from a performer winning an Oscar
increase the performer's life expectancy? Previous studies of this issue have
suffered from healthy performer survivor bias, that is, candidates who are
healthier will be able to act in more films and have more chance to win Oscar
Awards. To correct this bias, we adapt Robins' rank preserving structural
accelerated failure time model and $g$-estimation method.

Several approaches have been developed for forecasting mortality using the
stochastic model. In particular, the Lee-Carter model has become widely used
and there have been various extensions and modifications proposed to attain a
broader interpretation and to capture the main features of the dynamics of the
mortality intensity.

The Burning Index (BI) produced daily by the United States government's
National Fire Danger Rating System is commonly used in forecasting the hazard
of wildfire activity in the United States. However, recent evaluations have
shown the BI to be less effective at predicting wildfires in Los Angeles
County, compared to simple point process models incorporating similar
meteorological information.

We consider the estimation of wealth inequality measures with their
confidence interval, based on survey data with interval censoring. We rely on a
Bayesian hierarchical model. It consists of a model where, due to survey
sampling and unit nonresponse, the summaries of the wealth distribution of
households are observed with error; a mixture of multivariate models for the
wealth components where groups correspond to portfolios of assets; and a prior
on the parameters. A Gibbs sampler is used for numerical purposes to do the
inference. We apply this strategy to the French 2004 Wealth Survey.

Neural spike trains, which are sequences of very brief jumps in voltage
across the cell membrane, were one of the motivating applications for the
development of point process methodology. Early work required the assumption of
stationarity, but contemporary experiments often use time-varying stimuli and
produce time-varying neural responses. More recently, many statistical methods
have been developed for nonstationary neural point process data.

For many decades, ultra-high energy charged particles have been a puzzle for
particle physicists and astrophysicists. Nor the sites of production, nor the
mechanism responsible for the generation of these ultra-energetic `cosmic rays'
(CR) are currently known. They seem to arrive from random direction in the sky,
although the most energetic ones, which are not deflected much by the magnetic
fields, are supposed to point towards their source with good accuracy.

Deducing the structure of neural circuits is one of the central problems of
modern neuroscience. Recently-introduced calcium fluorescent imaging methods
permit experimentalists to observe network activity in large populations of
neurons, but these techniques provide only indirect observations of neural
spike trains, with limited time resolution and signal quality. In this work we
present a Bayesian approach for inferring neural circuitry given this type of
imaging data.

Effective connectivity analysis provides an understanding of the functional
organization of the brain by studying how activated regions influence one
other. We propose a nonparametric Bayesian approach to model effective
connectivity assuming a dynamic nonstationary neuronal system. Our approach
uses the Dirichlet process to specify an appropriate (most plausible according
to our prior beliefs) dynamic model as the "expectation" of a set of plausible
models upon which we assign a probability distribution. This addresses model
uncertainty associated with dynamic effective connectivity.

In the game of Scrabble, letter tiles are drawn uniformly at random from a
bag. The variability of possible draws as the game progresses is a source of
variation that makes it more likely for an inferior player to win a
head-to-head match against a superior player, and more difficult to determine
the true ability of a player in a tournament or contest.

While five-factor models of personality are widespread, there is still not
universal agreement on this as a structural framework. Part of the reason for
the lingering debate is its dependence on factor analysis. In particular,
derivation or refutation of the model via other statistical means is a
worthwhile project.

Vocal tract resonance characteristics in acoustic speech signals are
classically tracked using frame-by-frame point estimates of formant frequencies
followed by candidate selection and smoothing using dynamic programming methods
that minimize ad hoc cost functions. The goal of the current work is to provide
both point estimates and associated uncertainties of center frequencies and
bandwidths in a statistically principled state-space framework.

A series of ten plant species belonging to Magnoliopsida - Dicotyledons class
were analyzed in terms of chemical compounds distribution of abundance,
starting from the assumption that these distributions should give a picture of
similarities and differences between plants metabolism. From a pool of
theoretical distributions, log-normal distribution was selected giving the best
accuracy with the modeled phenomena and agreement with the observed data.

The Bernoulli Factory is an algorithm that takes as input a series of i.i.d.
Bernoulli random variables with an unknown but fixed success probability $p$,
and outputs a corresponding series of Bernoulli random variables with success
probability $f(p)$, where the function $f$ is known and defined on the interval
$[0,1]$. While several practical uses of the method have been proposed in Monte
Carlo applications, these require an implementation framework that is flexible,
general and efficient.

We explore the use of generalized t priors on regression coefficients to help
understand the nature of association signal within "hit regions" of genome-wide
association studies. The particular generalized t distribution we adopt is a
Student distribution on the absolute value of its argument. For low degrees of
freedom we show that the generalized t exhibits 'sparsity-prior' properties
with some attractive features over other common forms of sparse priors and
includes the well known double-exponential distribution as the degrees of
freedom tends to infinity.

Rejoinder to "A statistical analysis of multiple temperature proxies: Are
reconstructions of surface temperatures over the last 1000 years reliable?" by
B.B. McShane and A.J. Wyner [arXiv:1104.4002]

Logistic regression has been widely applied in the field of biostatistics for
a long time. It aims to model the conditional success probability of an event
of interest as the logit function of a linear combination of covariates, for
the sake of further interpretation of covariates and prediction of new
observation. In some applications, however, covariates of interest have a
natural structure, such as being a matrix, at the time of being collected. The
rows and columns of the covariate matrix would have different meanings, and
they must contain useful information regarding the response.

Discussion of "A statistical analysis of multiple temperature proxies: Are
reconstructions of surface temperatures over the last 1000 years reliable?" by
B.B. McShane and A.J. Wyner [arXiv:1104.4002]

Discussion of "A statistical analysis of multiple temperature proxies: Are
reconstructions of surface temperatures over the last 1000 years reliable?" by
B.B. McShane and A.J. Wyner [arXiv:1104.4002]

Discussion of "A statistical analysis of multiple temperature proxies: Are
reconstructions of surface temperatures over the last 1000 years reliable?" by
B.B. McShane and A.J. Wyner [arXiv:1104.4002]

Discussion of "A statistical analysis of multiple temperature proxies: Are
reconstructions of surface temperatures over the last 1000 years reliable?" by
B.B. McShane and A.J. Wyner [arXiv:1104.4002]

Discussion of "A statistical analysis of multiple temperature proxies: Are
reconstructions of surface temperatures over the last 1000 years reliable?" by
B.B. McShane and A.J. Wyner [arXiv:1104.4002]

Discussion of "A statistical analysis of multiple temperature proxies: Are
reconstructions of surface temperatures over the last 1000 years reliable?" by
B.B. McShane and A.J. Wyner [arXiv:1104.4002]

Discussion of "A statistical analysis of multiple temperature proxies: Are
reconstructions of surface temperatures over the last 1000 years reliable?" by
B.B. McShane and A.J. Wyner [arXiv:1104.4002]

We propose a new approach to analyze data that naturally lie on manifolds. We
focus on a special class of manifolds, called direct product manifolds, whose
intrinsic dimension could be very high. Our method finds a low-dimensional
representation of the manifold that can be used to find and visualize the
principal modes of variation of the data, as Principal Component Analysis (PCA)
does in linear spaces. The proposed method improves upon earlier manifold
extensions of PCA by more concisely capturing important nonlinear modes.

A new class of stochastic field models is constructed using nested stochastic
partial differential equations (SPDEs). The model class is computationally
efficient, applicable to data on general smooth manifolds, and includes both
the Gaussian Mat\'{e}rn fields and a wide family of fields with oscillating
covariance functions. Nonstationary covariance models are obtained by spatially
varying the parameters in the SPDEs, and the model parameters are estimated
using direct numerical optimization, which is more efficient than standard
Markov Chain Monte Carlo procedures.

We propose a computationally intensive method, the random lasso method, for
variable selection in linear models. The method consists of two major steps. In
step 1, the lasso method is applied to many bootstrap samples, each using a set
of randomly selected covariates. A measure of importance is yielded from this
step for each covariate. In step 2, a similar procedure to the first step is
implemented with the exception that for each bootstrap sample, a subset of
covariates is randomly selected with unequal selection probabilities determined
by the covariates' importance.

Recently, considerable interest has focused on variable selection methods in
regression situations where the number of predictors, $p$, is large relative to
the number of observations, $n$. Two commonly applied variable selection
approaches are the Lasso, which computes highly shrunk regression coefficients,
and Forward Selection, which uses no shrinkage. We propose a new approach,
"Forward-Lasso Adaptive SHrinkage" (FLASH), which includes the Lasso and
Forward Selection as special cases, and can be used in both the linear
regression and the Generalized Linear Model domains.

We are concerned with the estimation of the exterior surface and interior
summaries of tube-shaped anatomical structures. This interest is motivated by
two distinct scientific goals, one dealing with the distribution of HIV
microbicide in the colon and the other with measuring degradation in
white-matter tracts in the brain. Our problem is posed as the estimation of the
support of a distribution in three dimensions from a sample from that
distribution, possibly measured with error. We propose a novel tube-fitting
algorithm to construct such estimators.

Alternative splicing of gene transcripts greatly expands the functional
capacity of the genome, and certain splice isoforms may indicate specific
disease states such as cancer. Splice junction microarrays interrogate
thousands of splice junctions, but data analysis is difficult and error prone
because of the increased complexity compared to differential gene expression
analysis.

One of the challenges with functional data is incorporating spatial
structure, or local correlation, into the analysis. This structure is inherent
in the output from an increasing number of biomedical technologies, and a
functional linear model is often used to estimate the relationship between the
predictor functions and scalar responses. Common approaches to the ill-posed
problem of estimating a coefficient function typically involve two stages:
regularization and estimation.

A number of variable selection methods have been proposed involving nonconvex
penalty functions. These methods, which include the smoothly clipped absolute
deviation (SCAD) penalty and the minimax concave penalty (MCP), have been
demonstrated to have attractive theoretical properties, but model fitting is
not a straightforward task, and the resulting solutions may be unstable.

A statistical model for predicting individual house prices and constructing a
house price index is proposed utilizing information regarding sale price, time
of sale and location (ZIP code). This model is composed of a fixed time effect
and a random ZIP (postal) code effect combined with an autoregressive
component. The former two components are applied to all home sales, while the
latter is applied only to homes sold repeatedly. The time effect can be
converted into a house price index.

Climate models have become an important tool in the study of climate and
climate change, and ensemble experiments consisting of multiple climate-model
runs are used in studying and quantifying the uncertainty in climate-model
output. However, there are often only a limited number of model runs available
for a particular experiment, and one of the statistical challenges is to
characterize the distribution of the model output. To that end, we have
developed a multivariate hierarchical approach, at the heart of which is a new
representation of a multivariate Markov random field.

Mathematical models could be helpful in assisting the Indian Government's new
initiative of issuing biometric cards to its citizens. In this note, we look
into the role of mathematical models in estimating the missing, non-enumerated
population numbers, estimating annual numbers of cards required by age, gender
and regions in India. The linkage between National Population Register and
biometric cards is also highlighted. There are other scientific issues, namely,
electronic, data storage management, identity verification etc, which we do not
address in this paper.

In dynamic models of infectious disease transmission, typically various
mixing patterns are imposed on the so-called Who-Acquires-Infection-From-Whom
matrix (WAIFW). These imposed mixing patterns are based on prior knowledge of
age-related social mixing behavior rather than observations. Alternatively, one
can assume that transmission rates for infections transmitted predominantly
through non-sexual social contacts, are proportional to rates of conversational
contact which can be estimated from a contact survey.

We comment on a recent approach to stochastic inversion, which centers on a
concept known as "anchors" and conducts nonparametric estimation of the
likelihood of the anchors (along with other model parameters) with respect to
data obtained from field processes. The method is called "anchored inversion"
or (less accurately) "method of anchored distribution". Conceptual and
technical observations are made regarding the development, interpretation, and
use of this approach.

Air pollution is a great concern because of its impact on human health and on
the environment. Statistical models play an important role in improving
knowledge of this complex spatio-temporal phenomenon and in supporting public
agencies and policy makers. We focus on the class of hierarchical models that
provides a flexible framework for incorporating spatio-temporal interactions at
different hierarchical levels. The challenge is to choose a model that is
satisfactory in terms of goodness of fit, interpretability, parsimoniousness,
prediction capability and computational costs.

This is a note on logistic regression models and logistic kernel machine
models. It contains derivations to some of the expressions in a paper -- SNP
Set Analysis for Detecting Disease Association Using Exon Sequence Data --
submitted to BMC proceedings by these authors.

Technology has had an unquestionable impact on the way people watch sports.
As technology has evolved, so too has the knowledge of a casual sports fan. A
direct result of this evolution is the amount of statistical analysis in sport.
The goal of statistical analysis in sports is a simple one: to eliminate
subjective analysis. Over the past four decades, statistics have slowly
pervaded the viewing experience of sports. In this paper, we analyze previous
work that proposed metrics and models that seek to evaluate various aspects of
sports.

In several recent publications, Bettencourt, West and collaborators claim
that properties of cities such as gross economic production, personal income,
numbers of patents filed, number of crimes committed, etc., show super-linear
power-scaling with total population, while measures of resource use show
sub-linear power-law scaling.

Unbinned maximum likelihood is a common procedure for parameter estimation.
After parameters have been estimated, it is crucial to know whether the fit
model adequately describes the experimental data. Univariate Goodness of Fit
procedures have been thoroughly analyzed. In multi-dimensions, Goodness of Fit
test powers have rarely been studied on realistic problems. There is no
definitive answer to regarding which method is better. Test performance is
strictly related to specific analysis characteristics. In this work, a review
of multi-variate Goodness of Fit techniques is presented.

A method for estimating the axis of reflectional symmetry of an image
$f(x,y)$ on the unit disc $D=\{(x,y):x^2+y^2\leq1\}$ is proposed, given that
noisy data of $f(x,y)$ are observed on a discrete grid of edge width $\Delta$.
Our estimation procedure is based on minimizing over $\beta\in[0,\pi)$ the
$L_2$ distance between empirical versions of $f$ and $\tau_{\beta}f$, the image
of $f$ after reflection at the axis along $(\cos\beta,\sin\beta)$. Here, $f$
and $\tau_{\beta}f$ are estimated using truncated radial series of the Zernike
type.

We consider a well defined joint detection and parameter estimation problem.
By combining the Baysian formulation of the estimation subproblem with suitable
constraints on the detection subproblem we develop optimum one- and two-step
test for the joint detection/estimation case. The proposed combined strategies
have the very desirable characteristic to allow for the trade-off between
detection power and estimation efficiency. Our theoretical developments are
then applied to the problems of retrospective changepoint detection and MIMO
radar.

Group-based brain connectivity networks have great appeal for researchers
interested in gaining further insight into complex brain function and how it
changes across different mental states and disease conditions. Accurately
constructing these networks presents a daunting challenge given the
difficulties associated with accounting for inter-subject topological
variability. Viable approaches to this task must engender networks that capture
the constitutive topological properties of the group of subjects' networks that
it is aiming to represent.

This paper is motivated by a Eurobarometer survey on science knowledge. As
part of the survey, respondents were asked to rank sources of science
information in order of importance. The official statistical analysis of these
data however failed to use the complete ranking information. We instead propose
a method which treats ranked data as a set of paired comparisons which places
the problem in the standard framework of generalized linear models and also
allows respondent covariates to be incorporated. An extension is proposed to
allow for heterogeneity in the ranked responses.

Based on a data set obtained in a dental longitudinal study, conducted in
Flanders (Belgium), the joint time to caries distribution of permanent first
molars was modeled as a function of covariates. This involves an analysis of
multivariate continuous doubly-interval-censored data since: (i) the emergence
time of a tooth and the time it experiences caries were recorded yearly, and
(ii) events on teeth of the same child are dependent. To model the joint
distribution of the emergence times and the times to caries, we propose a
dependent Bayesian semiparametric model.

Within the private-values paradigm, we construct a tractable empirical model
of equilibrium behavior at first-price auctions when bidders' valuations are
potentially dependent, but not necessarily affiliated. We develop a test of
affiliation and apply our framework to data from low-price, sealed-bid auctions
held by the Department of Transportation in the State of Michigan to procure
road-resurfacing services: we do not reject the hypothesis of affiliation in
cost signals.

It has been estimated that about 30% of the genes in the human genome are
regulated by microRNAs (miRNAs). These are short RNA sequences that can
down-regulate the levels of mRNAs or proteins in animals and plants. Genes
regulated by miRNAs are called targets.

This paper describes a novel approach based on "proportional imputation" when
identical units produced in a batch have random but independent installation
and failure times. The current problem is motivated by a real life industrial
production-delivery supply chain where identical units are shipped after
production to a third party warehouse and then sold at a future date for
possible installation.

Doubly stochastic Poisson processes, also known as the Cox processes,
frequently occur in various scientific fields. In this article, motivated
primarily by analyzing Cox process data in biophysics, we propose a
nonparametric kernel-based inference method. We conduct a detailed study,
including an asymptotic analysis, of the proposed method, and provide
guidelines for its practical use, introducing a fast and stable regression
method for bandwidth selection.

During primary HIV infection, the kinetics of plasma virus concentrations and
CD4+ cell counts is very complex. Parametric and nonparametric models have been
suggested for fitting repeated measurements of these markers. Alternatively,
mechanistic approaches based on ordinary differential equations have also been
proposed. These latter models are constructed according to biological knowledge
and take into account the complex nonlinear interactions between viruses and
cells. However, estimating the parameters of these models is difficult.

Mass spectrometry-based proteomics has become the tool of choice for
identifying and quantifying the proteome of an organism. Though recent years
have seen a tremendous improvement in instrument performance and the
computational tools used, significant challenges remain, and there are many
opportunities for statisticians to make important contributions.

I do not remember when was the first time that I met Leo, but I have a clear
memory of going to Leo's office on the 4th floor of Evans Hall to talk to him
in my second year in Berkeley's Ph.D. program in 1986. The details of the
conversation are not retained but a visual image of his clean and orderly
office remains, in a stark contrast to a high entropy state of the same office
now being used by myself.

Massive datasets in the gigabyte and terabyte range combined with the
availability of increasingly sophisticated statistical tools yield analyses at
the boundary of what is computationally feasible. Compromising in the face of
this computational burden by partitioning the dataset into more tractable sizes
results in stratified analyses, removed from the context that justified the
initial data collection.

Large-scale statistical analysis of data sets associated with genome
sequences plays an important role in modern biology. A key component of such
statistical analyses is the computation of $p$-values and confidence bounds for
statistics defined on the genome. Currently such computation is commonly
achieved through ad hoc simulation measures. The method of randomization, which
is at the heart of these simulation procedures, can significantly affect the
resulting statistical conclusions.

During the period 1962--1964, I had a tenure track Assistant Professorship in
Mathematics at Cornell University in Ithaca, New York, where I did research in
probability theory, especially on linear diffusion processes. Being somewhat
lonely there and not liking the cold winter weather, I decided around the
beginning of 1964 to try to get a job in the Mathematics Department at UCLA, in
the city in which I was born and raised. At that time, Leo Breiman was an
Associate Professor in that department.

Leo Breiman was a unique character. There will not be another like him. I
consider it one of my great fortunes in life to have know and worked with him.
Along with John Tukey, Leo had the greatest influence on shaping my approach to
statistical problems. I did some of my best work collaborating with Leo, but
more importantly, we both had great fun doing it. I look back on those years
when we worked closely together with great fondness and regard them as among
the happiest and most fruitful of my professional career.

In 1994, I came to Berkeley and was fortunate to stay there three years,
first as a postdoctoral researcher and then as Neyman Visiting Assistant
Professor. For me, this period was a unique opportunity to see other aspects
and learn many more things about statistics: the Department of Statistics at
Berkeley was much bigger and hence broader than my home at ETH Z\"urich and I
enjoyed very much that the science was perhaps a bit more speculative.

With the rapid development of economy and the accelerated globalization
process, the aviation industry plays more and more critical role in today's
world, in both developed and developing countries. As the infrastructure of
aviation industry, the airport network is one of the most important indicators
of economic growth. In this paper, we investigate the evolution of Chinese
airport network (CAN) via complex network theory.

Under the sociological theory of homophily, people who are similar to one
another are more likely to interact with one another. Marketers often have
access to data on interactions among customers from which, with homophily as a
guiding principle, inferences could be made about the underlying similarities.
However, larger networks face a quadratic explosion in the number of potential
interactions that need to be modeled. This scalability problem renders
probability models of social interactions computationally infeasible for all
but the smallest networks.

In this paper, we review and apply several approaches to model selection for
analysis of variance models which are used in a credibility and insurance
context. The reversible jump algorithm is employed for model selection, where
posterior model probabilities are computed. We then apply this method to
insurance data from workers' compensation insurance schemes. The reversible
jump results are compared with the Deviance Information Criterion, and are
shown to be consistent.

A Bayesian multiple change-point model is proposed to analyse violations of
air quality standards by pollutants such as nitrogen oxides (NO2 and NO) and
carbon monoxide (CO). The model is built on the assumption that the occurrence
of threshold exceedances may be described by a non-homogeneous Poisson process
with a step rate function. Unlike earlier approaches, our model is not
restricted by a predetermined number of change-points, nor does it involve any
covariates.

This paper explores seasonal and long-memory time series properties by using
the seasonal fractional ARIMA model when the seasonal data has one and two
seasonal periods and short-memory counterparts. The stationarity and
invertibility parameter conditions are established for the model studied. To
estimate the memory parameters, the method given in Reisen, Rodrigues and Palma
(2006 a,b) is generalized here to deal with a time series with two seasonal
fractional long-memory parameters.

Studying the topology of so-called {\em real networks}, that is networks
obtained from sociological or biological data for instance, has become a major
field of interest in the last decade. One way to deal with it is to consider
that networks are built from small functional units called {\em motifs}, which
can be found by looking for small subgraphs whose numbers of occurrences in the
whole network of interest are surprisingly high.

High angular resolution diffusion imaging data is the observed characteristic
function for the local diffusion of water molecules in tissue. This data is
used to infer structural information in brain imaging. Non-parametric scalar
measures are proposed to summarize such data, and to locally characterize
spatial features of the diffusion probability density function (PDF), relying
on the geometry of the characteristic function. Summary statistics are defined
so that their distributions are, to first order, both independent of nuisance
parameters and also analytically tractable.

We develop a new principal components analysis (PCA) type dimension reduction
method for binary data. Different from the standard PCA which is defined on the
observed data, the proposed PCA is defined on the logit transform of the
success probabilities of the binary observations. Sparsity is introduced to the
principal component (PC) loading vectors for enhanced interpretability and more
stable extraction of the principal components. Our sparse PCA is formulated as
solving an optimization problem with a criterion function motivated from a
penalized Bernoulli likelihood.

In this article we describe a method for carrying out Bayesian estimation for
the double Pareto lognormal (dPlN) distribution which has been proposed as a
model for heavy-tailed phenomena. We apply our approach to estimate the
$\mathit{dPlN}/M/1$ and $M/\mathit{dPlN}/1$ queueing systems. These systems
cannot be analyzed using standard techniques due to the fact that the dPlN
distribution does not possess a Laplace transform in closed form.

Individual-level health data are often not publicly available due to
confidentiality; masked data are released instead. Therefore, it is important
to evaluate the utility of using the masked data in statistical analyses such
as regression. In this paper we propose a data masking method which is based on
spatial smoothing techniques. The proposed method allows for selecting both the
form and the degree of masking, thus resulting in a large degree of
flexibility.

We consider the problem of constructing optimal designs for population
pharmacokinetics which use random effect models. It is common practice in the
design of experiments in such studies to assume uncorrelated errors for each
subject.

Modeling species abundance patterns using local environmental features is an
important, current problem in ecology. The Cape Floristic Region (CFR) in South
Africa is a global hot spot of diversity and endemism, and provides a rich
class of species abundance data for such modeling. Here, we propose a
multi-stage Bayesian hierarchical model for explaining species abundance over
this region. Our model is specified at areal level, where the CFR is divided
into roughly $37{,}000$ one minute grid cells; species abundance is observed at
some locations within some cells.

Boundary distance (BD) plotting is a technique for making orientation
invariant comparisons of the spatial distribution of biochemical markers within
and across cells/nuclei. Marker expression is aggregated over points with the
same distance from the boundary. We present a suite of tools for improved data
analysis and statistical inference using BD plotting. BD is computed using the
Euclidean distance transform after presmoothing and oversampling of nuclear
boundaries. Marker distribution profiles are averaged using smoothing with
linearly decreasing bandwidth.

From the perspective of the network theory, the present work illustrates how
the parametric intrinsic geometric description exhibits an exact set of pair
correction functions and global correlation volume with and without the
inclusion of the imaginary power flow. The Gaussian fluctuations about the
equilibrium basis accomplish a well-defined, non-degenerate, curved regular
intrinsic Riemannian surfaces for the purely real and the purely imaginary
power flows and their linear combinations.