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作者：Kaiser, Mark S.; Lahiri, Soumendra N.; Nordman, Daniel J.
作者单位：Iowa State University; Texas A&M University System; Texas A&M University College Station
摘要：This paper develops goodness of fit statistics that can be used to formally assess Markov random field models for spatial data, when the model distributions are discrete or continuous and potentially parametric. Test statistics are formed from generalized spatial residuals which are collected over groups of nonneighboring spatial observations, called concliques. Under a hypothesized Markov model structure, spatial residuals within each conclique are shown to be independent and identically dist...
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作者：Bickel, P. J.; Kleijn, B. J. K.
作者单位：University of California System; University of California Berkeley; University of Amsterdam
摘要：In a smooth semiparametric estimation problem, the marginal posterior for the parameter of interest is expected to be asymptotically normal and satisfy frequentist criteria of optimality if the model is endowed with a suitable prior. It is shown that, under certain straightforward and interpretable conditions, the assertion of Le Cam's acclaimed, but strictly parametric, Bernstein-von Mises theorem [Univ. California Publ. Statist. 1 (1953) 277-329] holds in the semiparametric situation as well...
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作者：Goldberg, Yair; Kosorok, Michael R.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：We develop methodology for a multistage decision problem with flexible number of stages in which the rewards are survival times that are subject to censoring. We present a novel Q-learning algorithm that is adjusted for censored data and allows a flexible number of stages. We provide finite sample bounds on the generalization error of the policy learned by the algorithm, and show that when the optimal Q-function belongs to the approximation space, the expected survival time for policies obtain...
