1. Uniform Stability of a Particle Approximation of the Optimal Filter Derivative
- Author
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Arnaud Doucet, Sumeetpal S. Singh, Pierre Del Moral, Department of Statistics [Oxford], University of Oxford [Oxford], Statistical Laboratory [Cambridge], Department of Pure Mathematics and Mathematical Statistics (DPMMS), Faculty of mathematics Centre for Mathematical Sciences [Cambridge] (CMS), University of Cambridge [UK] (CAM)-University of Cambridge [UK] (CAM)-Faculty of mathematics Centre for Mathematical Sciences [Cambridge] (CMS), University of Cambridge [UK] (CAM)-University of Cambridge [UK] (CAM), Advanced Learning Evolutionary Algorithms (ALEA), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Dept of Statistics & Dept of Computer Science, University of British Columbia (UBC), University of Cambridge [UK] (CAM), Inria & Labri, Univ. Bordeaux, Apollo - University of Cambridge Repository, and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
FOS: Computer and information sciences ,Control and Optimization ,Mathematics - Statistics Theory ,Statistics Theory (math.ST) ,Derivative ,sequential Monte Carlo ,state-space models ,Statistics - Computation ,Stability (probability) ,Methodology (stat.ME) ,FOS: Mathematics ,recursive maximum likelihood ,Applied mathematics ,Computation (stat.CO) ,Statistics - Methodology ,ComputingMilieux_MISCELLANEOUS ,Mathematics ,[STAT.AP]Statistics [stat]/Applications [stat.AP] ,Signal processing ,hidden Markov models ,Estimation theory ,Applied Mathematics ,smoothing ,Filter (signal processing) ,Statistics::Computation ,[MATH.MATH-PR]Mathematics [math]/Probability [math.PR] ,Nonlinear system ,filter derivative ,Particle filter ,Smoothing - Abstract
Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.
- Published
- 2015