The American Community Survey (ACS) provides one-year (1y), three-year (3y)
and five-year (5y) multi-year estimates (MYEs) of various demographic and
economic variables for each "community", although the 1y and 3y may not be
available for communities with a small population. These survey estimates are
not truly measuring the same quantities, since they each cover different time
spans. Using some simplistic models, we demonstrate that comparing different
period-length MYEs results in spurious conclusions about trend movements.

We develop a simulation tool to support policy-decisions about healthcare for
chronic diseases in defined populations. Incident disease-cases are generated
in-silico from an age-sex characterised general population using standard
epidemiological approaches. A novel disease-treatment model then simulates
continuous life courses for each patient using discrete event simulation.
Ideally, the discrete event simulation model would be inferred from complete
longitudinal healthcare data via a likelihood or Bayesian approach.

With reference to the questionnaire adopted within the Italian project
"Ulisse" to assess health condition of elderly people, we investigate two
important issues: discriminant power and actual number of dimensions measured
by the items composing the questionnaire. The adopted statistical approach is
based on the joint use of the latent class model and a multidimensional item
response theory model based on the 2PL parametrization. The latter allows us to
account for the different discriminant power of these items.

We investigate the opinion dynamics by extending the majority rule model to a
preferential selection model, in which agents choose opinions with some
probability rather than absolutely follow the majority. In the model, agent $i$
agrees with one of binary opinions with the probability that is a power
function of the number of agents holding this opinion among agent $i$ and its
nearest neighbors, where an adjustable parameter $\alpha$ controls the degree
of preferential selection. We find that global consensus is unable to be
reached if $\alpha<1$.

Exponential random graph models are extremely difficult models to handle from
a statistical viewpoint, since their normalising constant, which depends on
model parameters, is available only in very trivial cases. We show how
inference can be carried out in a Bayesian framework using a MCMC algorithm,
which circumvents the need to calculate the normalising constants. We use a
population MCMC approach which accelerates convergence and improves mixing of
the Markov chain.

This paper examines the problem of estimating the parameters of a bandlimited
signal from samples corrupted by random jitter (timing noise) and additive iid
Gaussian noise, where the signal lies in the span of a finite basis. For the
presented classical estimation problem, the Cramer-Rao lower bound (CRB) is
computed, and an Expectation-Maximization (EM) algorithm approximating the
maximum likelihood (ML) estimator is developed. Simulations are performed to
study the convergence properties of the EM algorithm and compare the
performance both against the CRB and a basic linear estimator.

This paper presents a general stochastic model developed for a class of
cooperative wireless relay networks, in which imperfect knowledge of the
channel state information at the destination node is assumed. The framework
incorporates multiple relay nodes operating under general known non-linear
processing functions. When a non-linear relay function is considered, the
likelihood function is generally intractable resulting in the maximum
likelihood and the maximum a posteriori detectors not admitting closed form
solutions.

Studying the topology of so-called 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 motifs, which can be
found by looking for small subgraphs whose numbers of occurrences in the whole
network are surprisingly high. In this article, we propose to define motifs
through a local overrepresentation in the network and develop a statistic to
detect them without relying on simulations.

We apply multiple testing procedures to the validation of estimated default
probabilities in credit rating systems. The goal is to identify rating classes
for which the probability of default is estimated inaccurately, while still
maintaining a predefined level of committing type I errors as measured by the
familywise error rate (FWER) and the false discovery rate (FDR). For FWER, we
also consider procedures that take possible discreteness of the data resp. test
statistics into account.

Imputation of missing data in large regions of satellite imagery is necessary
when the acquired image has been damaged by shadows due to clouds, or
information gaps produced by sensor failure.

Spatial Independent Components Analysis (ICA) is increasingly used in the
context of functional Magnetic Resonance Imaging (fMRI) to study cognition and
brain pathologies. Salient features present in some of the extracted
Independent Components (ICs) can be interpreted as brain networks, but the
segmentation of the corresponding regions from ICs is still ill-controlled.
Here we propose a new ICA-based procedure for extraction of sparse features
from fMRI datasets. Specifically, we introduce a new thresholding procedure
that controls the deviation from isotropy in the ICA mixing model.

Inferential summaries of tree estimates are useful in the setting of
evolutionary biology, where phylogenetic trees have been built from DNA data
since the 1960's. In bioinformatics, psychometrics and data mining,
hierarchical clustering techniques output the same mathematical objects, and
practitioners have similar questions about the stability and `generalizability'
of these summaries.

In practical nonlinear filtering, the assessment of achievable filtering
performance is important. In this paper, we focus on the problem of efficiently
approximate the posterior Cramer-Rao lower bound (CRLB) in a recursive manner.
By using Gaussian assumptions, two types of approximations for calculating the
CRLB are proposed: An exact model using the state estimate as well as a
Taylor-series-expanded model using both of the state estimate and its error
covariance, are derived. Moreover, the difference between the two approximated
CRLBs is also formulated analytically.

Profile likelihood intervals of large quantiles in Extreme Value
distributions provide a good way to estimate these parameters of interest since
they take into account the asymmetry of the likelihood surface in the case of
small and moderate sample sizes; however they are seldom used in practice. In
contrast, maximum likelihood asymptotic (mla) intervals are commonly used
without respect to sample size.

The variance of the concentration in a sample can be estimated using
knowledge of the particle masses, concentrations and the parameter for the
dependent selection of particles. A number of variance estimators are
constructed including a class of hybrid estimators.

We show how to construct the best linear unbiased predictor (BLUP) for the
continuation of a curve in a spline-function model. We assume that the entire
curve is drawn from some smooth random process and that the curve is given up
to some cut point. We demonstrate how to compute the BLUP efficiently.
Confidence bands for the BLUP are discussed. Finally, we apply the proposed
BLUP to real-world call center data. Specifically, we forecast the continuation
of both the call arrival counts and the workload process at the call center of
a commercial bank.

We propose a new approach for clustering DNA features using array CGH data
from multiple tumor samples. We distinguish data-collapsing: joining contiguous
DNA clones or probes with extremely similar data into regions, from clustering:
joining contiguous, correlated regions based on a maximum likelihood principle.
The model-based clustering algorithm accounts for the apparent spatial patterns
in the data. We evaluate the randomness of the clustering result by a cluster
stability score in combination with cross-validation.

Portfolio allocation with gross-exposure constraint is an effective method to
increase the efficiency and stability of selected portfolios among a vast pool
of assets, as demonstrated in Fan et al (2008). The required high-dimensional
volatility matrix can be estimated by using high frequency financial data. This
enables us to better adapt to the local volatilities and local correlations
among vast number of assets and to increase significantly the sample size for
estimating the volatility matrix.

To account for the complex interplay between positive and negative dimensions
of experience in a well-defined framework, psychology needs theoretical models
associated with mathematical tools that integrate both dimensions. To this end,
we drew upon the Balanced States of Mind Model, an information-processing model
that relates quantitatively precise emotional balances of positive and negative
affects to psychopathology and optimal functioning.

This paper develops strategic foundations for an important statistical model
of random networks with heterogeneous expected degrees. Based on this, we show
how social networking services that subtly alter the costs and indirect
benefits of relationships can cause large changes in behavior and welfare. In
the model, agents who value friends and friends of friends choose how much to
socialize, which increases the probabilities of links but is costly.

I discuss the statistical methods used in a paper in a respected management
journal, in order to present a critique of how statistics is typically used in
this type of research. Three themes emerge. The value of any statistical
approach is limited by various factors, especially the restricted nature of the
population sampled.

We propose a method to generate a warning system for the early detection of
time clusters applied to public health surveillance data. This new method
relies on the evaluation of a return period associated to any new count of a
particular infection reported to a surveillance system. The method is applied
to Salmonella surveillance in France and compared to the model developed by
Farrington et al.

Assume that we observe a large number of curves, all of them with identical,
although unknown, shape, but with a different random shift. The objective is to
estimate the individual time shifts and their distribution. Such an objective
appears in several biological applications like neuroscience or ECG signal
processing, in which the estimation of the distribution of the elapsed time
between repetitive pulses with a possibly low signal-noise ratio, and without a
knowledge of the pulse shape is of interest.

Nonparametric estimation of the gap time distribution in a simple renewal
process may be considered a problem in survival analysis under particular
sampling frames corresponding to how the renewal process is observed. This note
describes several such situations where simple product limit estimators, though
inefficient, may still be useful.

I argue that we must distinguish between:

(0) the Three-Doors-Problem Problem [sic], which is to make sense of some
real world question of a real person.

(1) a large number of solutions to this meta-problem, i.e., many specific
Three-Doors-Problem problems, which are competing mathematizations of the
meta-problem (0).

As compared to load demand, frequent wind energy intermittencies produce
large short-term (sub 1-hr to 3-hr) deficits (and surpluses) in the energy
supply. These intermittent deficits pose systemic and structural risks that
will likely lead to energy deficits that have significant reliability
implications for energy system operators and consumers. This work provides a
toolset to help policy makers quantify these first-order risks. The thinking
methodology / framework shows that increasing wind energy penetration
significantly increases the risk of loss in California.

Researchers in many scientific fields make inferences from individuals to
larger groups. For many groups however, there is no list of members from which
to take a random sample. Respondent-driven sampling (RDS) is a relatively new
sampling methodology that circumvents this difficulty by using the social
networks of the groups under study. The RDS method has been shown to provide
unbiased estimates of population proportions given certain conditions. The
method is now widely used in the study of HIV-related high-risk populations
globally.

Bayesian inferences in high energy physics often use uniform prior
distributions for parameters about which little or no information is available
before data are collected. The resulting posterior distributions are therefore
sensitive to the choice of parametrization for the problem and may even be
improper if this choice is not carefully considered. Here we describe an
extensively tested methodology, known as reference analysis, which allows one
to construct parametrization-invariant priors that embody the notion of minimal
informativeness in a mathematically well-defined sense.

This paper studies business cycle patterns in UK sectoral output. It analyzes
the distinction between white noise processes and their non-white noise
counterparts in the frequency domain and further examines the associated
features and patterns for the process where white noise conditions are
violated. The characteristics of these sectors, arising from their
institutional features that may influence business cycles behavior and
patterns, are discussed.

A lot of financial data present slight negative skewness and excess kurtosis.
Whereas the kurtosis is usually addressed via student-t distributions, the
evident asymmetry in the data is often tacitly ignored. Here I introduce a new
class of generalized skewed distribution functions, which allows a very
flexible approach to model skewed data. Originating from a system-theory and an
input/output point of view, a non-linear transformation converts a random
variable X into a so called Lambert W random variable Y. Its skewness depends
on the skewness of X and a skew parameter delta.

It is now widely accepted that knowledge can be acquired from networks by
clustering their vertices according to connection profiles. Many methods have
been proposed. In this paper, we concentrate on a mixture model for graphs, the
so-called MixNet model, which is closely related to the stochastic block model.
The clustering of vertices and the estimation of MixNet model parameters have
been subject to previous work and numerous inference strategies such as
variational Expectation Maximization (EM) and classification EM have been
proposed.

Numerous statistics have been proposed for the measure of offensive ability
in major league baseball. While some of these measures may offer moderate
predictive power in certain situations, it is unclear which simple offensive
metrics are the most reliable or consistent. We address this issue with a
Bayesian hierarchical model for variable selection to capture which offensive
metrics are most predictive within players across time.

Missing data estimation is an important challenge with high-dimensional data
arranged in the form of a matrix. Typically this data matrix is transposable,
meaning that either the rows, columns or both can be treated as features. To
model transposable data, we present a modification of the matrix-variate
normal, the mean-restricted matrix-variate normal, in which the rows and
columns each have a separate mean vector and covariance matrix.

A generalization of Gy's theory for the variance of the fundamental sampling
error is reviewed. Practical situations where the generalized model potentially
leads to more accurate variance estimates are identified as: clustering of
particles, differences in densities or sizes of the particles or repulsive
inter-particle forces. Two general approaches for estimating an input parameter
for the generalized model are discussed. The first approach consists of
modelling based on physical properties of particles such as size, density and
electrostatic forces between particles.

The next generation of telescopes will acquire terabytes of image data on a
nightly basis. Collectively, these large images will contain billions of
interesting objects, which astronomers call sources. The astronomers' task is
to construct a catalog detailing the coordinates and other properties of the
sources. The source catalog is the primary data product for most telescopes and
is an important input for testing new astrophysical theories, but to construct
the catalog one must first detect the sources.

Large datasets with interactions between objects are common to numerous
scientific fields (i.e. social science, internet, biology...). The interactions
naturally define a graph and a common way to explore or summarize such dataset
is graph clustering. Most techniques for clustering graph vertices just use the
topology of connections ignoring informations in the vertices features.

The statistical analysis of complex networks is a challenging task, given
that appropriate statistical models and efficient computational procedures are
required in order for structures to be learned. One line of research has aimed
at developing mixture models for random graphs, and this strategy has been
successful in revealing structures in social and biological networks. The
principle of these models is to assume that the distribution of the edge values
follows a parametric distribution, conditionally on a latent structure which is
used to detect connectivity patterns.

We present a weighted-Lasso method to infer the parameters of a first-order
vector auto-regressive model that describes time course expression data
generated by directed gene-to-gene regulation networks. These networks are
assumed to own a priori internal structures of connectivity which drive the
inference method. Solution to the optimization problem is efficiently computed
using an active-set algorithm. We illustrate the performance both on synthetic
data and on the yeast regulation network by analyzing Spellman et al's dataset.

We present a new joint longitudinal and survival model aimed at estimating
the association between the risk of an event and the change in and history of a
biomarker that is repeatedly measured over time. We use cubic B-splines models
for the longitudinal component that lend themselves to straight-forward
formulations of the slope and integral of the trajectory of the biomarker. The
model is applied to data collected in a long term follow-up study of HIV
infected infants in Uganda. Estimation is carried out using MCMC methods.

The stationary distribution of allele frequencies under a variety of
Wright--Fisher $k$-allele models with selection and parent independent mutation
is well studied. However, the statistical properties of maximum likelihood
estimates of parameters under these models are not well understood. Under each
of these models there is a point in data space which carries the strongest
possible signal for selection, yet, at this point, the likelihood is unbounded.
This result remains valid even if all of the mutation parameters are assumed to
be known.

The statistical analysis of covariance matrix data is considered and, in
particular, methodology is discussed which takes into account the non-Euclidean
nature of the space of positive semi-definite symmetric matrices. The main
motivation for the work is the analysis of diffusion tensors in medical image
analysis. The primary focus is on estimation of a mean covariance matrix and,
in particular, on the use of Procrustes size-and-shape space. Comparisons are
made with other estimation techniques, including using the matrix logarithm,
matrix square root and Cholesky decomposition.

Spatially explicit data layers of tree species assemblages, referred to as
forest types or forest type groups, are a key component in large-scale
assessments of forest sustainability, biodiversity, timber biomass, carbon
sinks and forest health monitoring. This paper explores the utility of coupling
georeferenced national forest inventory (NFI) data with readily available and
spatially complete environmental predictor variables through spatially-varying
multinomial logistic regression models to predict forest type groups across
large forested landscapes.

Although anger is an important emotion that underlies much overt aggression
at great social cost, little is known about how to quantify anger or to specify
the relationship between anger and the overt behaviors that express it. This
paper proposes a novel statistical model which provides both a metric for the
intensity of anger and an approach to determining the quantitative relationship
between anger intensity and the specific behaviors that it controls.

We consider nonparametric estimation of the state price density encapsulated
in option prices. Unlike usual density estimation problems, we only observe
option prices and their corresponding strike prices rather than samples from
the state price density. We propose to model the state price density directly
with a nonparametric mixture and estimate it using least squares. We show that
although the minimization is taken over an infinitely dimensional function
space, the minimizer always admits a finite dimensional representation and can
be computed efficiently.

We develop a new estimation technique for recovering depth-of-field from
multiple stereo images. Depth-of-field is estimated by determining the shift in
image location resulting from different camera viewpoints. When this shift is
not divisible by pixel width, the multiple stereo images can be combined to
form a super-resolution image. By modeling this super-resolution image as a
realization of a random field, one can view the recovery of depth as a
likelihood estimation problem.

We visit the following problem: For a `generic' model of consumer choice
(namely, distributions over preference lists) and a limited amount of data on
how consumers actually make decisions (such as marginal preference
information), how may one predict revenues from offering a particular
assortment of choices? This is a central problem in operations research and
marketing. We present a framework to answer such questions and design a number
of tractable algorithms from a data and computational standpoint for the same.

We propose a novel approach for distributed statistical detection of
change-points in high-volume network traffic. We consider more specifically the
task of detecting and identifying the targets of Distributed Denial of Service
(DDoS) attacks. The proposed algorithm, called DTopRank, performs distributed
network anomaly detection by aggregating the partial information gathered in a
set of network monitors.

We develop the relational topic model (RTM), a hierarchical model of both
network structure and node attributes. We focus on document networks, where the
attributes of each document are its words, i.e., discrete observations taken
from a fixed vocabulary. For each pair of documents, the RTM models their link
as a binary random variable that is conditioned on their contents. The model
can be used to summarize a network of documents, predict links between them,
and predict words within them.

After an elementary derivation of the "time transformation", mapping a
counting process onto a homogeneous Poisson process with rate one, a brief
review of Ogata's goodness of fit tests is presented and a new test, the
"Wiener process test", is proposed. This test is based on a straightforward
application of Donsker's Theorem to the intervals of time transformed counting
processes. The finite sample properties of the test are studied by Monte Carlo
simulations.

One of the most important tasks in image processing problem and machine
vision is object recognition, and the success of many proposed methods relies
on a suitable choice of algorithm for the segmentation of an image. This paper
focuses on how to apply texture operators based on the concept of fractal
dimension and cooccurence matrix, to the problem of object recognition and a
new method based on fractal dimension is introduced.

International migration is now a significant driver of population change
across Europe but the methods available to estimate its true impact upon
sub-national areas remain inconsistent, constrained by inadequate systems of
measurement and data capture. In the absence of a population register for
England, official statistics on immigration and emigration are derived from a
combination of survey and census sources.