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作者：Tyler, DE
作者单位：Rutgers University System; Rutgers University New Brunswick
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作者：Zeng, DL
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：In many public health problems, an important goal is to identify the effect of some treatment/intervention on the risk of failure for the whole population. A marginal proportional hazards regression model is often used to analyze such an effect. When dependent censoring is explained by many auxiliary covariates, we utilize two working models to condense high-dimensional covariates to achieve dimension reduction. Then the estimator of the treatment effect is obtained by maximizing a pseudo-like...
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作者：Baraud, Y; Huet, S; Laurent, B
作者单位：Universite Cote d'Azur; Centre National de la Recherche Scientifique (CNRS); INRAE; Universite Paris Saclay; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite de Toulouse; Institut National des Sciences Appliquees de Toulouse
摘要：In this paper we propose a general methodology, based on multiple testing, for testing that the mean Of a Gaussian) vector in R(n) belongs to a convex set. We show that the test achieves its nominal level. and characterize a class of vectors over which the tests achieve a prescribed power, In the functional regression model this general methodology is applied to test some qualitative hypotheses oil the regression function. For example. we (est that the regression function is positive. increasi...
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作者：Gallegos, MT; Ritter, G
作者单位：University of Passau
摘要：Let there be given a contaminated list of n R-d-valued observations coming from g different, normally distributed populations with a common covariance matrix. We compute the ML-estimator with respect to a certain statistical model with n - r outliers for the parameters of the g populations it detects outliers and simultaneously partitions their complement into g clusters. It turns out that the estimator unites both the minimum-covariance-determinant rejection method and the well-known pooled d...
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作者：Zaslavsky, AM
作者单位：Harvard University; Harvard Medical School
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作者：Efromovich, S
作者单位：University of New Mexico
摘要：Estimation of the density of regression errors is a fundamental issue in regression analysis and it is typically explored via a parametric approach. This article uses a nonparametric approach with the mean integrated squared error (MISE) criterion. It solves a long-standing problem, formulated two decades ago by Mark Pinsker, about estimation of a nonparametric error density in a nonparametric regression setting with the accuracy of an oracle that knows the underlying regression errors. The So...
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作者：Tsybakov, AB; van de Geer, SA
作者单位：Sorbonne Universite; Universite Paris Cite; Leiden University; Leiden University - Excl LUMC
摘要：We consider the problem of adaptation to the margin in binary classification. We suggest a penalized empirical risk minimization classifier that adaptively attains, up to a logarithmic factor, fast optimal rates of convergence for the excess risk, that is, rates that can be faster than n(-1/2), where n is the sample size. We show that our method also gives adaptive estimators for the problem of edge estimation.
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作者：Kargin, V
作者单位：Cornerstone Research
摘要：The paper estimates the Chernoff rate for the efficiency of quantum IF hypothesis testing. For both joint and separate measurements, approximate bounds for the rate are given if both states are mixed, and exact expressions are derived if at least one of the states is pure. The efficiencies of tests with separate and joint measurements are compared. The results are illustrated by a test of quantum entanglement.
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作者：Asgharian, M; Wolfson, DB
作者单位：McGill University
摘要：Right censored survival data collected On a cohort of prevalent cases with constant incidence are length-biased, and may be used to estimate the length-biased (i.e., prevalent-case) survival function. When the incidence rate is constant, so-called stationarity of the incidence, it is more efficient to use this structure for unconditional statistical inference than to carry out an analysis by conditioning on the observed truncation times. It is well known that, due to the informative censoring ...
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作者：Künsch, HR
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：Recursive Monte Carlo filters, also called particle filters, are a powerful tool to perform computations in general state space models. We discuss and compare the accept-reject version with the more common sampling importance resampling version of the algorithm. In particular, we show how auxiliary variable methods and stratification can be used in the accept-reject version, and we compare different resampling techniques. In a second part, we show laws of large numbers and a central limit theo...
