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- catalog abstract "Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view. Prior distributions on model coefficients are parametrized by hyperparameters. Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters. The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model. The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian mixture distribution-two filter smoothing formula, and a Monte Carlo "particle-path tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.".
- catalog contributor b9785191.
- catalog contributor b9785192.
- catalog created "1996.".
- catalog date "1996".
- catalog date "1996.".
- catalog dateCopyrighted "1996.".
- catalog description "Includes bibliographical references (p.231-251) and index.".
- catalog description "Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view. Prior distributions on model coefficients are parametrized by hyperparameters. Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters. The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model. The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian mixture distribution-two filter smoothing formula, and a Monte Carlo "particle-path tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.".
- catalog extent "x, 261 p. :".
- catalog identifier "0387948198 (pbk. : alk. paper)".
- catalog isPartOf "Lecture notes in statistics (Springer-Verlag) ; v. 116.".
- catalog isPartOf "Lecture notes in statistics ; 116".
- catalog issued "1996".
- catalog issued "1996.".
- catalog language "eng".
- catalog publisher "New York : Springer,".
- catalog subject "519.5/5 20".
- catalog subject "Global analysis (Mathematics).".
- catalog subject "QA280 .K58 1996".
- catalog subject "Statistics.".
- catalog subject "Time-series analysis.".
- catalog title "Smoothness priors analysis of time series / Genshiro Kitagawa, Will Gersch.".
- catalog type "text".