The Annals of Statistics

最新文章

• A limit theory for long-range dependence and statistical inference on related models AI翻译
  作者：Hosoya, Y
  作者单位：Tohoku University
  摘要：This paper provides limit theorems for multivariate, possibly non-Gaussian stationary processes whose spectral density matrices may have singularities not restricted at the origin, applying those limiting results to the asymptotic theory of parameter estimation and testing for statistical models of long-range dependent processes. The central limit theorems are proved based on the assumption that the innovations of the stationary processes satisfy certain mixing conditions for their conditional...
• Optimal repeated measurements designs: The linear optimality equations AI翻译
  作者：Kushner, HB
  作者单位：Nathan Kline Institute for Psychiatric Research
  摘要：In approximate design theory, necessary and sufficient conditions that a repeated measurements design be universally optimal are given as linear equations whose unknowns are the proportions of subjects on the treatment sequences. Both the number of periods and the number of treatments in the designs are arbitrary, as is the covariance matrix of the normal response model. The existence of universally optimal symmetric designs is proved; the single linear equation which the proportions satisfy i...
• Local likelihood and local partial likelihood in hazard regression AI翻译
  作者：Fan, JQ; Gijbels, I; King, M
  作者单位：Chinese University of Hong Kong; Universite Catholique Louvain; University of North Carolina; University of North Carolina Chapel Hill
  摘要：In survival analysis, the relationship between a survival time and a covariate is conveniently modeled with the proportional hazards regression model. This model usually assumes that the covariate has a log-linear effect on the hazard function. In this paper we consider the proportional hazards regression model with a nonparametric risk effect. We discuss estimation of the risk function and its derivatives in two cases: when the baseline hazard function is parametrized and when it is not param...
• Asymptotic expansion of M-estimators with long-memory errors AI翻译
  作者：Koul, HL; Surgailis, D
  作者单位：Michigan State University; Vilnius University
  摘要：This paper obtains a higher-order asymptotic expansion of a class of M-estimators of the one-sample location parameter when the errors form a long-memory moving average; A suitably standardized difference between an M-estimator and the sample mean is shown to have a limiting distribution. The nature of the limiting distribution depends on the range of the dependence parameter theta. If for example, 1/3 < theta < 1, then a suitably standardized difference between the sample median and the sampl...
• Empirical likelihood methods with weakly dependent processes AI翻译
  作者：Kitamura, Y
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：This paper studies the method of empirical likelihood in models with weakly dependent processes. In such cases, if the likelihood function is formulated as if the data process were independent, obviously empirical likelihood fails. We propose to use empirical likelihood of blocks of observations to solve this problem in a nonparametric manner. This method of blockwise empirical likelihood preserves the dependence of data, and the resulting likelihood ratios can be used to construct asymptotica...
• On automatic boundary corrections AI翻译
  作者：Cheng, MY; Fan, JQ; Marron, JS
  作者单位：National Chung Cheng University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Many popular curve estimators based on smoothing have difficulties caused by boundary effects. These effects are visually disturbing in practice and can play a dominant role in theoretical analysis. Local polynomial regression smoothers are known to correct boundary effects automatically. Some analogs are implemented for density estimation and the resulting estimators also achieve automatic boundary corrections. In both settings of density and regression estimation, we investigate best weight ...
• Semiparametric likelihood ratio inference AI翻译
  作者：Murphy, SA; van der Vaart, AW
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Vrije Universiteit Amsterdam
  摘要：Likelihood ratio tests and related confidence intervals for a real parameter in the presence of an infinite dimensional nuisance parameter are considered. In all cases, the estimator of the real parameter has an asymptotic normal distribution. However, the estimator of the nuisance parameter may not be asymptotically Gaussian or may converge to the true parameter value at a slower rate than the square root of the sample size. Nevertheless the likelihood ratio statistic is shown to possess an a...
• Universally optimal designs with blocksize p x 2 and correlated observations AI翻译
  作者：Uddin, N; Morgan, JP
  作者单位：Tennessee Technological University; Old Dominion University
  摘要：This paper addresses the problems of determination and construction of universally optimal designs in two-dimensional blocks of size p x 2, assuming within-block observations are correlated. Generalized least-squares estimation of treatment contrasts is considered in four fixed block-effects models: (I) with fixed row and column effects, (II) with the row effects only, (III) with the column effects only, and (IV) with neither row nor column effects. For a general dependence structure and p = 2...
• Nonlinear confounding in high-dimensional regression AI翻译
  作者：Li, KC
  作者单位：University of California System; University of California Los Angeles
  摘要：It is not uncommon to find nonlinear patterns in the scatterplots of regressor variables. But how such findings affect standard regression analysis remains largely unexplored. This article offers a theory on nonlinear confounding, a term for describing the situation where a certain nonlinear relationship in regressors leads to difficulties in modeling and related analysis of the data. The theory begins with a measure of nonlinearity between two regressor variables. It is then used to assess no...
• Consistency of maximum likelihood estimators in general random effects models for binary data AI翻译
  作者：Butler, SM; Louis, TA
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Kentucky
  摘要：We consider a general random effects model for repeated binary measures, assuming a latent linear model with any class of mixing distributions. The latent model is assumed to have the Laird-Ware structure, but the random effects may be from any specified class of multivariate distributions and the error vector may have any specified continuous distribution. Elementwise threshold crossing then gives the observed vector of binary outcomes. Special cases of this model include recently discussed m...