Hypotheses testing and posterior concentration rates for semi-Markov processes

被引：0
作者：
I. Votsi
G. Gayraud
V. S. Barbu
N. Limnios
机构：
[1] Institut du Risque et de l’Assurance,Laboratoire Manceau de Mathématiques
[2] Le Mans Université, LMM EA 3263
[3] Université de Technologie de Compiègne,Laboratoire de Mathématiques Raphaël Salem
[4] LMAC (Laboratory of Applied Mathematics of Compiègne),undefined
[5] Université de Rouen-Normandie,undefined
[6] UMR 6085,undefined
来源：
Statistical Inference for Stochastic Processes | 2021年 / 24卷
关键词：
Bayesian nonparametrics; Posterior concentration rates; Semi-Markov processes; Semi-Markov kernels; Robust statistical tests;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we adopt a nonparametric Bayesian approach and investigate the asymptotic behavior of the posterior distribution in continuous-time and general state space semi-Markov processes. In particular, we obtain posterior concentration rates for semi-Markov kernels. For the purposes of this study, we construct robust statistical tests between Hellinger balls around semi-Markov kernels and present some specifications to particular cases, including discrete-time semi-Markov processes and countable state space Markov processes. The objective of this paper is to provide sufficient conditions on priors and semi-Markov kernels that enable us to establish posterior concentration rates.
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页码：707 / 732
页数：25
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