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作者：Rubin, DB; Frangakis, CE
作者单位：Harvard University; Johns Hopkins University
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作者：Ding, AA; Hwang, JTG
作者单位：Northeastern University; Cornell University
摘要：We discuss a technique that provides prediction intervals based on a model called an empirical linear model. The technique, high-dimensional empirical linear prediction (HELP), involves principal component analysis, factor analysis and model selection. In fact, a special case of the empirical model is the factor analysis model. A factor analysis model does not generally aim at prediction, however. Therefore, HELP can be viewed as a technique that provides prediction (and confidence) intervals ...
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作者：Geys, H; Molenberghs, G; Ryan, LM
作者单位：Hasselt University; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute
摘要：The primary goal of this article is to determine benchmark doses based on the ethylene glycol study, which comprises data from a developmental toxicity study in mice. Because the data involve a vector of malformation indicators, a flexible model for multivariate clustered data is required. An exponential family model is considered and pseudolikelihood-based inferential tools are proposed, hence avoiding excessive computational requirements.
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作者：Ng, ETM; Cook, RJ
作者单位：University of Waterloo
摘要：Tests of homogeneity are being increasingly used for the analysis of event time data, but relatively little attention has been paid to their distributional properties in settings with small to moderate sample sizes. Here: we consider tests of homogeneity for recurrent event data in which the null model is a Poisson process and the alternative is a mixed Poisson process. We examine score and adjusted score statistics in the contest of parametric and semiparametric regression models, where the a...
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作者：Shen, XT; Shi, J; Wong, WH
作者单位：University System of Ohio; Ohio State University; Chinese Academy of Sciences; University of California System; University of California Los Angeles
摘要：Consider a semiparametric regression model Y = f (theta, X, epsilon), where f is a known function, theta is an unknown vector, epsilon consists of a random error and possibly of some unobserved variables, and the distribution F(.) of (epsilon, X) is unspecified. This article introduces, in a general setting, new methodology for estimating theta and F(.). The proposed method constructs a profile likelihood defined on random-level sets (a random sieve). The proposed method is related to empirica...
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作者：Fan, JQ; Zhang, CM
作者单位：University of California System; University of California Los Angeles; University of North Carolina; University of North Carolina Chapel Hill
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作者：Yang, LJ; Marron, JS
作者单位：Michigan State University; University of North Carolina; University of North Carolina Chapel Hill
摘要：Transformation from a parametric family can improve the performance of kernel density estimation. In this article we give two data-driven estimators for the optimal transformation parameter. We demonstrate that multiple families of transformations can be used at the same time, and there can be benefits to iterating this process. The transformation scheme can be expected to first pick the right transformation family and then pick the optimal parameter. Insight as to the performance of the metho...
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作者：Yang, S; Prentice, RL
作者单位：Texas Tech University System; Texas Tech University; Fred Hutchinson Cancer Center
摘要：For fitting the proportional odds regression model with right-censored survival times, we introduce some weighted empirical odds functions. These functions are solutions of some self-consistency equations and have a nice martingale representation. From these functions, several classes of new regression estimators, such as the pseudo-maximum likelihood estimator, martingale residual-based estimators, and minimum distance estimators, are derived. These estimators have desirable properties such a...
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作者：Tarpey, T
作者单位：University System of Ohio; Wright State University Dayton
摘要：I examine the self-consistency of a principal component axis; that is, when a distribution is centered about a principal component axis. A principal component axis of a random vector X is self-consistent if each point on the axis corresponds to the mean of X given that X projects orthogonally onto that point. A large class of symmetric multivariate distributions are examined in terms of self-consistency of principal component subspaces. Elliptical distributions are characterized by the preserv...
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作者：Qu, Y; Hadgu, A
作者单位：Cleveland Clinic Foundation; Centers for Disease Control & Prevention - USA
