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作者：Ren, JJ; Gu, MG
作者单位：Tulane University; McGill University
摘要：The M-estimators are proposed for the linear regression model with random design when the response observations are doubly censored. The proposed estimators are constructed as some functional of a Campbell-type estimator (F) over cap(n) for a bivariate distribution function based on data which are doubly censored in one coordinate. We establish strong uniform consistency and asymptotic normality of (F) over cap(n) and derive the asymptotic normality of the proposed regression M-estimators thro...
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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...
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作者：Brown, LD; Hwang, JTG; Munk, A
作者单位：University of Pennsylvania; Cornell University; Ruhr University Bochum
摘要：It is shown that the standard two one-sided tests procedure for bioequivalence is a biased test. Better tests exist. In this paper, an unbiased alpha-level test and other tests which are uniformly more powerful than the two one-sided tests procedure are constructed. Its power can be noticeably larger than that of the alpha-level two one-sided tests procedure.
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作者：Hsieh, F
作者单位：National Taiwan University
摘要：Pertaining to the estimating equations proposed by Tsiatis, based on the Linear rank test, we show the existence of local confounding between the baseline hazard function and the covariates. Due to the local confounding, an estimating equation in Tsiatis' family with a larger time point of truncation could contain less information about the regression parameter than the estimating equation with a smaller time point of truncation. This phenomenon further indicates significant loss of efficiency...
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作者：Kong, A; Liu, JS; Wong, WH
作者单位：University of Chicago; Stanford University; University of California System; University of California Los Angeles
摘要：By noting the connection with h-sample U-statistics, we find a simple decomposition of the variance of the cross-match estimate, which can be regarded as a generalization of Efron and Stein. We apply the decomposition in assessing efficiencies of several plans of using the weighted samples from an importance scheme. The applications of the formula to multiple imputations lead to a method of crossing jointly imputed data to gain more accuracy.
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作者：Devroye, L; Lugosi, G
作者单位：McGill University; Pompeu Fabra University
摘要：We introduce a method to select a smoothing factor for kernel density estimation such that, for all densities in all dimensions, the L-1 error of the corresponding kernel estimate is not larger than three times the error of the estimate with the optimal smoothing factor plus a constant times root log n/n, where n is the sample size, and the constant depends only on the complexity of the kernel used in the estimate. The result is nonasymptotic, that is, the bound is valid for each n. The estima...
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作者：Lepski, OV; Spokoiny, VG
作者单位：Humboldt University of Berlin; Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics
摘要：The problem of optimal adaptive estimation of a function at a given point from noisy data is considered. Two procedures are proved to be asymptotically optimal for different settings. First we study the problem of bandwidth selection for nonparametric pointwise kernel estimation with a given kernel. We propose a bandwidth selection procedure and prove its optimality in the asymptotic sense. Moreover, this optimality is stated not only among kernel estimators with a variable bandwidth. The resu...
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作者：Low, MG
作者单位：University of Pennsylvania
摘要：An inequality is given for the expected length of a confidence interval given that a particular distribution generated the data and assuming that the confidence interval has a given coverage probability over a family of distributions. As a corollary, attempts to adapt to the regularity of the true density within derivative smoothness classes cannot improve the rate of convergence of the length of the confidence interval over minimax fixed-length intervals and still maintain uniform coverage pr...
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作者：Ye, JM; Duan, NH
作者单位：University of Chicago; RAND Corporation
摘要：We propose simple estimators for the transformation function Delta and the distribution function F of the error for the model Delta(Y) = alpha + X beta + epsilon. It is proved that these estimators are consistent and can achieve the unusual n(-1/2) rate of convergence on any finite interval under some regularity conditions. We show that our estimators are more attractive than another class of estimators proposed by Horowitz. Interesting decompositions of the estimators are obtained. The estima...
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作者：Walther, G
作者单位：Stanford University
摘要：A new method for smoothing a multivariate data set is introduced that is based on a simple geometric operation. This method is applied to the problem of estimating level sets of a density and minimum volume sets with given probability content. Building on existing techniques, the resulting estimator combines excellent theoretical and computational properties: It converges with the minimax rates (up to log factors) in most cases where these rates are known and, at the same time, it can be compu...
