Monte Carlo smoothing for nonlinear time series

Journal article

Abstract:: We develop methods for performing smoothing computations in general state-space models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are presented for generation of sample realizations of historical state sequences. This is carried out in a forward-filtering backward-smoothing procedure that can be viewed as the nonlinear, non-Gaussian counterpart of standard Kalman filter-based simulation smoothers in the linear Gaussian case. Convergence in the mean squared error sense of the smoothed trajectories is proved, showing the validity of our proposed method. The methods are tested in a substantial application for the processing of speech signals represented by a time-varying autoregression and parameterized in terms of time-varying partial correlation coefficients, comparing the results of our algorithm with those from a simple smoother based on the filtered trajectories.

Journal:: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION More from this journal
Volume:: 99
Issue:: 465
Pages:: 156-168
Publication date:: 2004-03-01
DOI:: 10.1198/016214504000000151
EISSN:: 1537-274X
ISSN:: 0162-1459

Language:: English
Keywords:: nonlinear time series

particle filter

sequential Monte Carlo

non-Gaussian time series

Bayesian inference

state-space model
Pubs id:: pubs:190594
UUID:: uuid:a8793c16-500e-47f3-8b63-139ab6af6001
Local pid:: pubs:190594
Source identifiers:: 190594
Deposit date:: 2012-12-19
ARK identifier:: ark:/29072/ora_a8793c16500e47f38b63139ab6af6001

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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