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Monte Carlo Kalman filter and smoothing for multivariate discrete state space models
Author(s) -
Song Peter XueKun
Publication year - 2000
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315971
Subject(s) - kalman filter , smoothing , ensemble kalman filter , monte carlo method , state space representation , state space , extended kalman filter , latent variable , mathematics , state variable , invariant extended kalman filter , gaussian , multivariate statistics , alpha beta filter , fast kalman filter , computer science , algorithm , statistics , physics , moving horizon estimation , quantum mechanics , thermodynamics
The author studies state space models for multivariate binomial time series, focussing on the development of the Kalman filter and smoothing for state variables. He proposes a Monte Carlo approach employing the latent variable representation which transplants the classical Kalman filter and smoothing developed for Gaussian state space models to discrete models and leads to a conceptually simple and computationally convenient approach. The method is illustrated through simulations and concrete examples.