Particle-based online estimation of tangent filters with application to parameter estimation in nonlinear state-space models

Jimmy Olsson () and Johan Westerborn Alenlöv ()
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Jimmy Olsson: KTH Royal Institute of Technology
Johan Westerborn Alenlöv: KTH Royal Institute of Technology

Annals of the Institute of Statistical Mathematics, 2020, vol. 72, issue 2, No 9, 545-576

Abstract: Abstract This paper presents a novel algorithm for efficient online estimation of the filter derivatives in general hidden Markov models. The algorithm, which has a linear computational complexity and very limited memory requirements, is furnished with a number of convergence results, including a central limit theorem with an asymptotic variance that can be shown to be uniformly bounded in time. Using the proposed filter derivative estimator, we design a recursive maximum likelihood algorithm updating the parameters according the gradient of the one-step predictor log-likelihood. The efficiency of this online parameter estimation scheme is illustrated in a simulation study.

Keywords: Parameter estimation; Recursive maximum likelihood; State-space models; Tangent filter; Sequential Monte Carlo methods; Central limit theorem; Particle filters (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10463-018-0698-1

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