Smoothness Priors Analysis of Time Series

Name: Smoothness Priors Analysis of Time Series
SKU: 2527827
Price: 381.00 NZD
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Paperback / softback

Series:

Lecture Notes in Statistics

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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.

Release date NZ

August 9th, 1996

Author

Audiences

Postgraduate, Research & Scholarly
Professional & Vocational

Illustrations

X, 280 p.

Pages

280

Series

Lecture Notes in Statistics

Dimensions

155x235x14

ISBN-13

9780387948195

Product ID

2527827

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