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• Nonparametric estimation of component distributions in a multivariate mixture AI翻译
  作者：Hall, P; Zhou, XH
  作者单位：Australian National University; US Department of Veterans Affairs; Veterans Health Administration (VHA); Vet Affairs Puget Sound Health Care System; University of Washington; University of Washington Seattle
  摘要：Suppose k-variate data are drawn from a mixture of two distributions, each having independent components. It is desired to estimate the univariate marginal distributions in each of the products, as well as the mixing proportion. This is the setting of two-class, fully parametrized latent models that has been proposed for estimating the distributions of medical test results when disease status is unavailable. The problem is one of inference in a mixture of distributions without training data, a...
• Separation and completeness properties for AMP chain graph Markov models (vol 29, pg 1751, 2001) AI翻译
  作者：Levitz, M; Perlman, MD; Madigan, D
• Model selection in nonparametric regression AI翻译
  作者：Wegkamp, M
  作者单位：Yale University
  摘要：Model selection using a penalized data-splitting device is studied in the context of nonparametric regression. Finite sample bounds under mild conditions are obtained. The resulting estimates are adaptive for large classes of functions.
• The complex Wishart distribution and the symmetric group AI翻译
  作者：Graczyk, P; Letac, G; Massam, H
  作者单位：Universite d'Angers; Universite de Toulouse; Universite Toulouse III - Paul Sabatier; York University - Canada
  摘要：Let V be the space of (r, r) Hermitian matrices and let Omega be the cone of the positive definite ones. We say that the random variable S, taking its values in (Ω) over bar, has the complex Wishart distribution gamma(p,sigma) if E(exp trace(thetaS)) = (det(I-r - sigmatheta))(-p), where sigma and sigma(-1) - theta are in Omega, and where p = 1, 2,..., r - 1 or p > r - 1. In this paper, we compute all moments of S and S-1. The techniques involve in particular the use of the irreducible charact...
• Thresholding estimators for linear inverse problems and deconvolutions AI翻译
  作者：Kalifa, K; Mallat, S
  作者单位：Institut Polytechnique de Paris; ENSTA Paris; Ecole Polytechnique; New York University
  摘要：Thresholding algorithms in an orthonormal basis are studied to estimate noisy discrete signals degraded by a linear operator whose inverse is not bounded. For signals in a set Theta, sufficient conditions are established on the basis to obtain a maximum risk with minimax rates of convergence. Deconvolutions with kernels having a Fourier transform which vanishes at high frequencies are examples of unstable inverse problems, where a thresholding in a wavelet basis is a suboptimal estimator. A ne...
• Wavelet threshold estimation for additive regression models AI翻译
  作者：Zhang, SL; Wong, MY
  作者单位：Michigan Technological University; Heilongjiang University; Hong Kong University of Science & Technology
  摘要：Additive regression models have turned out to be useful statistical tools in the analysis of high-dimensional data. The attraction of such models is that the additive component can be estimated with the same optimal convergence rate as a one-dimensional nonparametric regression. However, this optimal property holds only when all the additive components have the same degree of homogeneous smoothness. In this paper, we propose a two-step wavelet thresholding estimation process in which the estim...
• Adaptive tests of linear hypotheses by model selection AI翻译
  作者：Baraud, Y; Huet, S; Laurent, B
  作者单位：Universite PSL; Ecole Normale Superieure (ENS); INRAE; Universite Paris Saclay; Universite Paris Saclay
  摘要：We propose a new test, based on model selection methods, for testing that the expectation of a Gaussian vector with n independent components belongs to a linear subspace of R-n against a nonparametric alternative. The testing procedure is available when the variance of the observations is unknown and does not depend on any prior information on the alternative. The properties of the test are nonasymptotic and we prove that the test is rate optimal [up to a possible log(n) factor] over various c...
• Asymptotics for generalized estimating equations with large cluster sizes AI翻译
  作者：Xie, MG; Yang, YN
  作者单位：Rutgers University System; Rutgers University New Brunswick; Rockefeller University
  摘要：Generalized estimating equations are used in regression analysis of longitudinal data, where observations on each subject are correlated. Statistical analysis using such methods is based on the asymptotic properties of regression parameter estimators. This paper presents asymptotic results when either the number of independent subjects or the cluster sizes (the number of observations on each subject) or both go to infinity. A set of (information matrix based) general conditions is developed, w...
• Multivariate saddlepoint tail probability approximations AI翻译
  作者：Kolassa, JE
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：This paper presents a saddlepoint approximation to the cumulative distribution function of a random vector. The proposed approximation has accuracy comparable to that of existing expansions valid in two dimensions, and may be applied to random vectors of arbitrary length, subject only to the requirement that the distribution approximated either have a density or be confined to a lattice, and have a cumulant generating function. The result is derived by directly inverting the multivariate momen...
• Wavelet thresholding for non-necessarily Gaussian noise:: Idealism AI翻译
  作者：Averkamp, R; Houdré, C
  作者单位：University of Freiburg; Universite Paris-Est-Creteil-Val-de-Marne (UPEC); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); University System of Georgia; Georgia Institute of Technology