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作者：Jensen, JL; Petersen, NV
作者单位：Aarhus University
摘要：State space models is a very general class of time series models capable of modelling dependent observations in a natural and interpretable way. Inference in such models has been studied by Bickel, Ritov and Ryden, who consider hidden Markov models, which are special kinds of state space models, and prove that the maximum likelihood estimator is asymptotically normal under mild regularity conditions. In this paper we generalize the results of Bickel, Ritov and Ryden to state space models, wher...
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作者：Hsing, T
作者单位：National University of Singapore; Texas A&M University System; Texas A&M University College Station
摘要：Sliced inverse regression (SIR), formally introduced by Li, is a very general procedure for performing dimension reduction in nonparametric regression. This paper considers a version of SIR in which the slices are determined by nearest neighbors and the response variable takes value possibly in a multidimensional space. It is shown, under general conditions, that the effective dimension reduction space can be estimated with rate n(-1/2) where n is the sample size.
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作者：Nishiyama, Y
作者单位：Research Organization of Information & Systems (ROIS); Institute of Statistical Mathematics (ISM) - Japan
摘要：Some sufficient conditions to establish the rate of convergence of certain M-estimators in a Gaussian white noise model are presented. They are applied to some concrete problems, including jump point estimation and nonparametric maximum Likelihood estimation, for the regression function. The results are shown by means of a maximal inequality for continuous martingales and some techniques developed recently in the context of empirical processes.
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作者：Loader, CR
作者单位：Alcatel-Lucent; Lucent Technologies
摘要：Bandwidth selection for procedures such as kernel density estimation and local regression have been widely studied over the past decade. Substantial evidence has been collected to establish superior performance of modern plug-in methods in comparison to methods such as cross validation: this has ranged from detailed analysis of rates of convergence, to simulations, to superior performance on real datasets. In this work we take a detailed look at some of this evidence, looking into the sources ...
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作者：Barron, A; Schervish, MJ; Wasserman, L
作者单位：Yale University; Carnegie Mellon University
摘要：We give conditions that guarantee that the posterior probability of every Hellinger neighborhood of the true distribution tends to 1 almost surely. The conditions are (1) a requirement that the prior not put high mass near distributions with very rough densities and (2) a requirement that the prior put positive mass in Kullback-Leibler neighborhoods of the true distribution. The results are based on the idea of approximating the set of distributions with a finite-dimensional set of distributio...
