Smoothness Priors Analysis of Time Series (Paperback, 1996 ed.)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Lecture Notes in Statistics, 116
Release date:	August 1996
First published:	1996
Authors:	Genshiro Kitagawa • Will Gersch
Dimensions:	235 x 155 x 14mm (L x W x T)
Format:	Paperback
Pages:	280
Edition:	1996 ed.
ISBN-13:	978-0-387-94819-5
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics
LSN:	0-387-94819-8
Barcode:	9780387948195

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