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Monte Carlo Kalman filter and smoothing for multivariate discrete state space models
Authors:Peter Xue‐Kun Song
Abstract: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.
Keywords:Binomial time series  Kalman filter  Monte Carlo  smoothing  state space model
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