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作者：Jiang, JM
作者单位：University System of Ohio; Case Western Reserve University
摘要：We prove that for all unconfounded balanced mixed models of the analysis of variance, estimates of variance components parameters that maximize the (restricted) Gaussian Likelihood are consistent and asymptotically normal-and this is true whether normality is assumed or not. For a general (nonnormal) mixed model, we show estimates of the variance components parameters that maximize the (restricted) Gaussian likelihood over a sequence of approximating parameter spaces (i.e., a sieve) constitute...
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作者：Dette, H; Röder, I
作者单位：Ruhr University Bochum; Leipzig University
摘要：In this paper efficient designs are determined when Anderson's procedure is applied in order to identify the degree of a multivariate polynomial regression model. It is shown that the optimal designs are very closely related to model robust designs which maximize a weighted p-mean of D-efficiencies. As a consequence we obtain designs with high efficiency for model discrimination and for the statistical analysis in the identified model.
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作者：Hall, P; Weissman, I
作者单位：Australian National University; Technion Israel Institute of Technology
摘要：Applications of extreme value theory to problems of statistical inference typically involve estimating tail probabilities well beyond the range of the data, without the benefit of a concise mathematical model for the sampling distribution. The available model is generally only an asymptotic one. That is, an approximation to probabilities of extreme deviation is supposed, which is assumed to become increasingly accurate as one moves further from the range of the data, but whose concise accuracy...
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作者：He, XM; Wang, G
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; DePaul University
摘要：Contours of depth often provide a good geometrical understanding of the structure of a multivariate dataset. They are also useful in robust statistics in connection with generalized medians and data ordering. If the data constitute a random sample from a spherical or elliptic distribution, the depth contours are generally required to converge to spherical or elliptical shapes. We consider contour constructions based on a notion of data depth and prove a uniform contour convergence theorem unde...
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作者：Bhattacharya, PK; Zhao, PL
作者单位：University of California System; University of California Davis; Merck & Company
摘要：In a partial linear model, the dependence of a response variate Y on covariates (W, X) is given by Y = W beta + eta(X)+ E, where E is independent of (W, X) with densities g and f, respectively. In this paper an asymptotically efficient estimator of beta is constructed solely under mild smoothness assumptions on the unknown eta, f and g, thereby removing the assumption of finite residual variance on which all least-squares-type estimators available in the literature are based.
