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作者：BESAG, J
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作者：CHANG, IS; HSIUNG, CA
作者单位：Academia Sinica - Taiwan
摘要：This paper indicates that a minor modification of the maximum likelihood estimate of Vardi, Shepp and Kaufman can be regarded as a step in the standard nonparametric MLE by the method of sieves and establishes the asymptotic consistency for it. This method of sieves suggests that the number of pixels needs to be in line with the number of detectors in order to avoid poor image reconstructions. a simulation study is also presented to support this suggestion.
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作者：DOSS, H
摘要：In the problem of estimating an unknown distribution function F in the presence of censoring, one can use a nonparametric estimator such as the Kaplan-Meier estimator, or one can consider parametric modeling. There are many situations where physical reasons indicate that a certain parametric model holds approximately. In these cases a nonparametric estimator may be very inefficient relative to a parametric estimator. On the other hand, if the parametric model is only a crude approximation to t...
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作者：ROBERT, CP
作者单位：Universite de Rouen Normandie
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作者：HALL, P; FISHER, NI; HOFFMANN, B
作者单位：Commonwealth Scientific & Industrial Research Organisation (CSIRO)
摘要：We describe kernel methods for estimating the covariance function of a stationary stochastic process, and show how to ensue that the estimator has the positive semidefiniteness property. From a practical viewpoint, our method is significant because it does not demand a parametric model for covariance. From a technical angle, our results exhibit a striking departure from those in more familiar cases of kernel estimation. For example, in the context of covariance estimation, kernel estimators ca...
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作者：FOSTER, DP; GEORGE, EI
作者单位：University of Texas System; University of Texas Austin
摘要：A new criterion is proposed for the evaluation of variable selection procedures in multiple regression. This criterion, which we call the risk inflation, is based on an adjustment to the risk. Essentially, the risk inflation is the maximum increase in risk due to selecting rather than knowing the ''correct'' predictors. A new variable selection procedure is obtained which, in the case of orthogonal predictors, substantially improves on AIC, C-p and BIC and is close to optimal. In contrast to A...
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作者：DONNELL, DJ; BUJA, A; STUETZLE, W
作者单位：AT&T; Nokia Corporation; Nokia Bell Labs; University of Washington; University of Washington Seattle
摘要：Additive principal components are a nonlinear generalization of linear principal components. Their distinguishing feature is that linear forms Sigma(i) alpha(i)X(i) are replaced with additive functions Sigma(i) phi(i)(X(i)). A considerable amount of flexibility for fitting data is gained when linear methods are replaced with additive ones. Our interest is in the smallest principal components, which is somewhat uncommon. Smallest additive principal components amount to data descriptions in term...
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作者：DOSS, H
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作者：TIERNEY, L
摘要：Several Markov chain methods are available for sampling from a posterior distribution. Two important examples are the Gibbs sampler and the Metropolis algorithm. In addition, several strategies are available for constructing hybrid algorithms. This paper outlines some of the basic methods and strategies and discusses some related theoretical and practical issues. On the theoretical side, results from the theory of general state space Markov chains can be used to obtain convergence rates, laws ...
