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Opis

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.

Szczegóły książki

Kategoria Książki po angielsku Mathematics & science Mathematics Probability & statistics

• Pełny tytuł: Smoothness Priors Analysis of Time Series
• Autor: Genshiro Kitagawa, Willi Gersch
• Język: Angielski
• Oprawa: Miękka
• Liczba stron: 280
• EAN: 9780387948195
• ISBN: 0387948198
• ID: 01384169
• Wydawca: Springer-Verlag New York Inc.
• Waga: 880 g
• Wymiary: 235 × 155 × 16 mm
• Data wydania: 09. August 1996

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