bokomslag Smoothness Priors Analysis of Time Series
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Smoothness Priors Analysis of Time Series

Genshiro Kitagawa Will Gersch

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  • 280 sidor
  • 1996
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.
  • Författare: Genshiro Kitagawa, Will Gersch
  • Format: Pocket/Paperback
  • ISBN: 9780387948195
  • Språk: Engelska
  • Antal sidor: 280
  • Utgivningsdatum: 1996-08-01
  • Förlag: Springer-Verlag New York Inc.