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Pei Wang

  1. Bootstrap Inference for Network Construction.

    Wed, 11/23/2011 - 15:01 — SomNambul
    Authors: Pei Wang, Li Hsu, Jie Peng, Shuang Li
    Subjects: Methodology
    Abstract

    Regularization techniques are widely used for tackling
    high-dimension-low-sample-size problems. Yet, finding the right amount of
    regularization can be challenging, especially in the unsupervised setting such
    as structure learning problems where traditional methods such as BIC or
    cross-validation often do not work well. In this paper, we propose a new method
    --- Bootstrap Inference for Network COnstruction (BINCO) --- to infer networks
    by directly controlling the false discovery rates (FDRs) of the selected edges.
    This method utilizes the idea of model aggregation.

  2. The joint graphical lasso for inverse covariance estimation across multiple classes.

    Thu, 11/03/2011 - 15:01 — SomNambul
    Authors: Pei Wang, Patrick Danaher, Daniela M. Witten
    Subjects: Methodology
    Abstract

    We consider the problem of estimating multiple related but distinct graphical
    models on the basis of a high-dimensional data set with observations that
    belong to distinct classes. A motivating example occurs in the analysis of gene
    expression data for tissue samples with and without cancer. In this case, we
    might wish to estimate a gene expression network for the normal tissue and a
    gene expression network for the tumor tissue.

  3. A note on logistic regression and logistic kernel machine models.

    Mon, 03/07/2011 - 16:01 — SomNambul
    Authors: Pei Wang, Jie Peng, Ru Wang
    Subjects: Applications
    Abstract

    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.

  4. Statistical Methods for Analyzing Tissue Microarray Images - Algorithmic Scoring and Co-training.

    Wed, 02/02/2011 - 16:01 — SomNambul
    Authors: Pei Wang, Donghui Yan, Beatrice S. Knudsen, Michael Linden, Timothy W. Randolph
    Subjects: Methodology
    Abstract

    Recent advances in tissue microarray technology have allowed
    immunohistochemistry to become a powerful medium-to-high throughput analysis
    tool, particularly for the validation of diagnostic and prognostic biomarkers.
    However, as study size grows, the manual evaluation of these assays becomes a
    prohibitive limitation; it vastly reduces throughput and greatly increases
    variability and expense. We propose an algorithm - Tissue Array Co-Occurrence
    Matrix Analysis (TACOMA) - for quantifying cellular phenotypes based on
    textural regularity summarized by local inter-pixel relationships.

  5. A generalized Fourier approach to estimating the null parameters and proportion of non-null effects in large-scale multiple testing.

    Fri, 11/20/2009 - 16:01 — SomNambul
    Authors: Pei Wang, Jiashun Jin, Jie Peng
    Subjects: Statistics
    Abstract

    In a recent paper (Efron (2004)), Efron pointed out that an important issue
    in large-scale multiple hypothesis testing is that the null distribution may be
    unknown and need to be estimated. Consider a Gaussian mixture model, where the
    null distribution is known to be normal but both null parameters--the mean and
    the variance--are unknown. We address the problem with a method based on
    Fourier transformation.

  6. Learning networks from high dimensional binary data: An application to genomic instability data.

    Thu, 08/27/2009 - 13:23 — SomNambul
    Authors: Pei Wang, Dennis L. Chao, Li Hsu
    Subjects: Methodology
    Abstract

    Genomic instability, the propensity of aberrations in chromosomes, plays a
    critical role in the development of many diseases. High throughput genotyping
    experiments have been performed to study genomic instability in diseases. The
    output of such experiments can be summarized as high dimensional binary
    vectors, where each binary variable records aberration status at one marker
    locus. It is of keen interest to understand how these aberrations interact with
    each other. In this paper, we propose a novel method, \texttt{LogitNet}, to
    infer the interactions among aberration events.

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