The scan sampler for time series models

Piet De Jong*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

An algorithm, called the scan sampler, is developed and discussed. The scan sampler has a variety of uses for time series analysis based on the state space model with nonGaussian observations. The algorithm is based on the Kaiman filter/smoothing algorithm. It can be used for Bayesian inference using Markov chain Monte Carlo and to find posterior modes.

Original languageEnglish
Pages (from-to)929-937
Number of pages9
JournalBiometrika
Volume84
Issue number4
Publication statusPublished - 1997
Externally publishedYes

Keywords

  • Exponential family
  • Generalised linear time series
  • Gibbs sampling
  • Kaiman filter
  • Markov chain monte carlo
  • Nonparametric regression
  • Smoothing filter
  • State space model
  • Stochastic volatility
  • Tobit models

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