The scan sampler for time series models

Piet De Jong

The scan sampler for time series models

Piet De Jong^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	929-937
Number of pages	9
Journal	Biometrika
Volume	84
Issue number	4
Publication status	Published - 1997
Externally published	Yes

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

Cite this

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title = "The scan sampler for time series models",

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.",

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",

author = "{De Jong}, Piet",

year = "1997",

language = "English",

volume = "84",

pages = "929--937",

journal = "Biometrika",

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T1 - The scan sampler for time series models

AU - De Jong, Piet

PY - 1997

Y1 - 1997

N2 - 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.

AB - 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.

KW - Exponential family

KW - Generalised linear time series

KW - Gibbs sampling

KW - Kaiman filter

KW - Markov chain monte carlo

KW - Nonparametric regression

KW - Smoothing filter

KW - State space model

KW - Stochastic volatility

KW - Tobit models

UR - http://www.scopus.com/inward/record.url?scp=0011628998&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0011628998

SN - 0006-3444

VL - 84

SP - 929

EP - 937

JO - Biometrika

JF - Biometrika

IS - 4

ER -

The scan sampler for time series models

Abstract

Keywords

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