Tuned iterated filtering

Abstract:
Iterated filtering is an algorithm for estimating parameters in partially observed Markov process (POMP) models. The real-world performance of the algorithm depends on several tuning parameters. We propose a simple method for optimizing the parameter governing the joint dynamics of the hidden parameter process (called the Sigma matrix). The tuning is implemented using a fixed-lag sequential Monte Carlo expectation maximization (EM) algorithm. We introduce two different versions of the tuning parameter, the approximately estimated Sigma matrix, and a normalized version of the same matrix. Our simulations show that the finite-sample performance for the normalized matrix outperform the standard iterated filter, while the naive version is doing more harm than good. (C) 2013 Elsevier B.V. All rights reserved.