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作者：BROWN, LD; GREENSHTEIN, E
作者单位：Bar Ilan University
摘要：Let X(i) be i.i.d. X(i) approximately F(theta). For some parametric families {F(theta)}, we describe a monotonicity property of Bayes sequential procedures for the decision problem H0: theta = 0 versus H1: theta not-equal 0. A surprising counterexample is given in the case where F(theta) is N(theta, 1).
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作者：YANG, S
摘要：In this paper we consider the product-limit estimator of the survival distribution function in the context of independent but nonidentically distributed censoring times. An upper bound on the mean square increment of the stopped Kaplan-Meier process is obtained. Also, a representation is given for the ratio of the survival distribution function to the product-limit estimator as the product of a bounded process and a martingale. From this representation bounds on the mean square of the ratio an...
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作者：FRANKE, J; HARDLE, W
作者单位：Universite Catholique Louvain
摘要：An approach to bootstrapping kernel spectral density estimates is described which is based on resampling from the periodogram of the original data. We show that it is asymptotically valid under suitable conditions, and we illustrate its performance for a medium-sized time series sample with a small simulation study.
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作者：HJORT, NL; FENSTAD, G
摘要：Suppose theta(n) is a strongly consistent estimator for theta(0) in some i.i.d. situation. Let N(epsilon) and Q(epsilon) be, respectively, the last n and the total number of n for which theta(n) is at least epsilon away from theta(0). The limit distributions for epsilon(2)N(epsilon) and epsilon(2)Q(epsilon) as epsilon goes to zero are obtained under natural and weak conditions. The theory covers both parametric and nonparametric cases, multidimensional parameters and general distance functions...
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作者：LOPUHAA, HP
摘要：We propose an affine equivariant estimator of multivariate location that combines a high breakdown point and a bounded influence function with high asymptotic efficiency. This proposal is basically a location M-estimator based on the observations obtained after scaling with an affine equivariant high breakdown covariance estimator. The resulting location estimator will inherit the breakdown point of the initial covariance estimator and within the location-covariance model only the M-estimator ...
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作者：HALLIN, M; MELARD, G; MILHAUD, X
摘要：Normal approximations, as provided by permutational central limit theorems, conditionally can be arbitrarily bad. Such approximations therefore are poorly suited to the construction of critical values for Pitman (permutation) tests. A classical remedy consists in substituting a beta approximation (over the appropriate conditional interval range) for the normal one. Whereas deriving permutational extreme values for usual, nonserial statistics is generally straightforward, the corresponding prob...
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作者：LOW, MG
作者单位：University of California System; University of California Berkeley
摘要：White noise models often renormalize exactly yielding optimal rates of convergence for pointwise nonparametric functional estimation problems. Similar rescaling ideas lead to a sequence of experiments appropriate for pointwise density estimation problems.
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作者：MAJUMDAR, D
摘要：Bayes A-optimal designs for the one-way and the two-way elimination of heterogeneity models and optimal GAMMA-minimax designs for the one-way elimination of heterogeneity model for experiments to compare test treatments with a control are given. Properties such as robustness, of these designs are studied. Optimality results are derived in the exact theory setup.
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作者：BENHENNI, K; CAMBANIS, S
摘要：The problem of estimating the integral of a stochastic process from observations at a finite number of sampling points is considered. Sacks and Ylvisaker found a sequence of asymptotically optimal sampling designs for general processes with exactly 0 and 1 quadratic mean (q.m.) derivatives using optimal-coefficient estimators, which depend on the process covariance. These results were extended to a restricted class of processes with exactly K q.m. derivatives, for all K = 0, 1, 2,..., by Euban...
