Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Smoothness Priors Analysis of Time Series

Smoothness Priors Analysis of Time Series

Gersch, Will
Kitagawa, Genshiro

Paperback

Series: Lecture Notes in Statistics, Book 116

General GardeningGeneral MathematicsProbability & Statistics

ISBN10: 0387948198
ISBN13: 9780387948195
Publisher: Springer
Published: Aug 9 1996
Pages: 280
Weight: 0.87
Height: 0.59 Width: 6.14 Depth: 9.21
Language: English
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.

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