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Michael R. Kosorok

  1. Support Vector Regression for Right Censored Data.

    Пт, 02/24/2012 - 15:01 — SomNambul
    Authors: Michael R. Kosorok, Yair Goldberg
    Subjects: Machine Learning
    Abstract

    We develop a unified approach for support vector machines for classification
    and regression in which the outcomes are a function of the survival times
    subject to right censoring. We present a novel support-vector regression
    algorithm that is adjusted for censored data. We provide finite sample bounds
    on the generalization error of the algorithm. We prove risk consistency for a
    wide class of probability measures and study learning rates. We apply the
    general methodology to estimation of the (truncated) mean, median, quantiles,
    and for classification problems.

  2. Penalized Q-Learning for Dynamic Treatment Regimes.

    Пнд, 08/29/2011 - 15:01 — SomNambul
    Authors: Rui Song, Michael R. Kosorok, Weiwei Wang, Donglin Zeng
    Subjects: Methodology
    Abstract

    A dynamic treatment regime effectively incorporates both accrued information
    and long-term effects of treatment from specially designed clinical trials. As
    these become more and more popular in conjunction with longitudinal data from
    clinical studies, the development of statistical inference for optimal dynamic
    treatment regimes is a high priority.

  3. Simultaneous critical values for $t$-tests in very high dimensions.

    Пт, 02/11/2011 - 16:01 — SomNambul
    Authors: Michael R. Kosorok, Hongyuan Cao
    Subjects: Statistics
    Abstract

    This article considers the problem of multiple hypothesis testing using
    $t$-tests. The observed data are assumed to be independently generated
    conditional on an underlying and unknown two-state hidden model. We propose an
    asymptotically valid data-driven procedure to find critical values for
    rejection regions controlling the $k$-familywise error rate ($k$-FWER), false
    discovery rate (FDR) and the tail probability of false discovery proportion
    (FDTP) by using one-sample and two-sample $t$-statistics.

  4. Discussion of: Brownian distance covariance.

    Ср, 10/06/2010 - 15:01 — SomNambul
    Authors: Michael R. Kosorok
    Subjects: Applications
    Abstract

    We discuss briefly the very interesting concept of Brownian distance
    covariance developed by Sz\'{e}kely and Rizzo [Ann. Appl. Statist. (2009), to
    appear] and describe two possible extensions. The first extension is for high
    dimensional data that can be coerced into a Hilbert space, including certain
    high throughput screening and functional data settings. The second extension
    involves very simple modifications that may yield increased power in some
    settings.

  5. On asymptotically optimal tests under loss of identifiability in semiparametric models.

    Втр, 08/25/2009 - 22:41 — SomNambul
    Authors: Rui Song, Michael R. Kosorok, Jason P. Fine
    Subjects: gr. Statistics
    Abstract

    We consider tests of hypotheses when the parameters are not identifiable
    under the null in semiparametric models, where regularity conditions for
    profile likelihood theory fail. Exponential average tests based on integrated
    profile likelihood are constructed and shown to be asymptotically optimal under
    a weighted average power criterion with respect to a prior on the
    nonidentifiable aspect of the model. These results extend existing results for
    parametric models, which involve more restrictive assumptions on the form of
    the alternative than do our results.

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