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Ya'Acov Ritov

  1. The Best Linear Unbiased Estimator for Continuation of a Function.

    Ср, 05/12/2010 - 15:01 — SomNambul
    Authors: Ya'Acov Ritov, Yair Goldberg, Avishai Mandelbaum
    Subjects: Applications
    Abstract

    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.

  2. On the trasductive arguments in statistics.

    Втр, 03/09/2010 - 16:01 — SomNambul
    Authors: Ya'Acov Ritov
    Subjects: Statistics
    Abstract

    The paper argues that a part of the current statistical discussion is not
    based on the standard firm foundations of the field. Among the examples we
    consider are prediction into the future, semi-supervised classification, and
    causality inference based on observational data.

  3. Sparse Empirical Bayes Analysis (SEBA).

    Втр, 12/01/2009 - 16:01 — SomNambul
    Authors: Ya'Acov Ritov, Natalia Bochkina
    Subjects: Machine Learning
    Abstract

    We consider a joint processing of $n$ independent sparse regression problems.
    Each is based on a sample $(y_{i1},x_{i1})...,(y_{im},x_{im})$ of $m$ \iid
    observations from $y_{i1}=x_{i1}\t\beta_i+\eps_{i1}$, $y_{i1}\in \R$, $x_{i
    1}\in\R^p$, $i=1,...,n$, and $\eps_{i1}\dist N(0,\sig^2)$, say. $p$ is large
    enough so that the empirical risk minimizer is not consistent. We consider
    three possible extensions of the lasso estimator to deal with this problem, the
    lassoes, the group lasso and the RING lasso, each utilizing a different
    assumption how these problems are related.

  4. Importance Sampling for rare events and conditioned random walks.

    Пнд, 10/12/2009 - 10:31 — SomNambul
    Authors: Michel Broniatowski, Ya'Acov Ritov
    Subjects: Statistics
    Abstract

    This paper introduces a new Importance Sampling scheme, called Adaptive
    Twisted Importance Sampling, which is adequate for the improved estimation of
    rare event probabilities in he range of moderate deviations pertaining to the
    empirical mean of real i.i.d. summands. It is based on a sharp approximation of
    the density of long runs extracted from a random walk conditioned on its end
    value.

  5. Importance Sampling for rare events and conditioned random walks.

    Пнд, 10/12/2009 - 10:31 — SomNambul
    Authors: Michel Broniatowski, Ya'Acov Ritov
    Subjects: Statistics
    Abstract

    This paper introduces a new Importance Sampling scheme, called Adaptive
    Twisted Importance Sampling, which is adequate for the improved estimation of
    rare event probabilities in he range of moderate deviations pertaining to the
    empirical mean of real i.i.d. summands. It is based on a sharp approximation of
    the density of long runs extracted from a random walk conditioned on its end
    value.

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