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作者：Dawid, AP; Lauritzen, SL
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作者：JACOBSEN, M; KEIDING, N
作者单位：University of Copenhagen
摘要：Heitjan and Rubin proposed a concept, ''coarsening at random,'' generalizing Rubin's theory of missing at random. Their analysis was done in discrete sample spaces. We propose a generalization to general sample spaces. Among Heitjan and Rubin's applications was right-censoring in survival analysis. We discuss the application of the generalized theory to various censoring patterns in continuous time and connect to the modern theory of random censoring.
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作者：WONG, WH; SHEN, XT
作者单位：University System of Ohio; Ohio State University
摘要：Let Y-1,...,Y-n be independent identically distributed with density p(o) and let F be a space of densities. We show that the supremum of the likelihood ratios Pi(i=1)(n)p(Y-i)/p(o)(Y-i), where the supremum is over p is an element of F with \\p(1/2) - p(o)(1/2)\\(2) greater than or equal to epsilon, is exponentially small with probability exponentially dose to 1. The exponent is proportional to n epsilon(2). The only condition required for this to hold is that epsilon exceeds a value determined...
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作者：Drees, H
摘要：Consider a distribution function that belongs to the weak domain of attraction of an extreme value distribution. The extreme value index beta will be estimated by mixtures of Pickands estimators, where the weights are generated by a probability measure which satisfies a certain integrability condition. We prove a functional limit theorem for a process of Pickands estimators and asymptotic normality of the refined Pickands estimator. For negative beta the new estimator is asymptotically superio...
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作者：Schick, A
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作者：CHEN, H
作者单位：National Taiwan University
摘要：We use the method of maximum likelihood and regression splines to derive estimates of the parametric and nonparametric components of semiparametric generalized linear models. The resulting estimators of both components are shown to be consistent. Also, the asymptotic theory for the estimator of the parametric component is derived, indicating that the parametric component can be estimated efficiently without under-smoothing the nonparametric component.
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作者：HALL, WJ; WIJSMAN, RA; GHOSH, JK
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作者：Clarke, B; Ghosh, JK
作者单位：Indian Statistical Institute; Indian Statistical Institute Kolkata
摘要：For various applications one wants to know the asymptotic behavior of w(theta\(X) over bar), the posterior density of a parameter theta given the mean (X) over bar of the data rather than the full data set. Here we show that w(theta\(X) over bar) is asymptotically normal in an L(1) sense, and rye identify the mean of the limiting normal and its asymptotic variance. The main results are first proved assuming that X(1),..., X(n),... are independent and identical; suitable modifications to obtain...
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作者：DONG, JP; SIMONOFF, JS
作者单位：New York University
摘要：A geometric combination estimator is proposed for d-dimensional ordinal contingency tables. The proposed estimator is nonnegative. It is shown that, assuming sufficient smoothness and boundary conditions for the underlying probabilities, the rate of convergence of mean summed squared error (MSSE) of this estimator is O(K-1N-8/((d+8))) for d-dimensional tables (d less than or equal to 4) with K cells and sample size N. This rate is optimal under the smoothness assumptions, and is faster than th...
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作者：CSORGO, S; MIELNICZUK, J
作者单位：Polish Academy of Sciences; Institute of Computer Science of the Polish Academy of Sciences
摘要：We consider the fixed-design regression model with long-range dependent normal errors and show that the finite-dimensional distributions of the properly normalized Gasser-Muller and Priestley-Chao estimators of the regression function converge to those of a white noise process. Furthermore, the distributions of the suitably renormalized maximal deviations over an increasingly finer grid converge to the Gumbel distribution. These results contrast with our previous findings for the corresponding...
