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Smoothness Priors Analysis of Time Series

  • Book
  • © 1996

Overview

Authors:
  1. Genshiro Kitagawa

    1. The Institute of Statistical Mathematics, Tokyo, Japan

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    You can also search for this author in PubMed   Google Scholar

  2. Will Gersch

    1. Department of Information and Computer Science, University of Hawaii, Honolulu, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

Part of the book series: Lecture Notes in Statistics (LNS, volume 116)

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-x
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  2. Introduction

    • Genshiro Kitagawa, Will Gersch
    Pages 1-8

  3. Modeling Concepts and Methods

    • Genshiro Kitagawa, Will Gersch
    Pages 9-26

  4. The Smoothness Priors Concept

    • Genshiro Kitagawa, Will Gersch
    Pages 27-32

  5. Scalar Least Squares Modeling

    • Genshiro Kitagawa, Will Gersch
    Pages 33-53

  6. Linear Gaussian State Space Modeling

    • Genshiro Kitagawa, Will Gersch
    Pages 55-65

  7. General State Space Modeling

    • Genshiro Kitagawa, Will Gersch
    Pages 67-89

  8. Applications of Linear Gaussian State Space Modeling

    • Genshiro Kitagawa, Will Gersch
    Pages 91-104

  9. Modeling Trends

    • Genshiro Kitagawa, Will Gersch
    Pages 105-121

  10. Seasonal Adjustment

    • Genshiro Kitagawa, Will Gersch
    Pages 123-135

  11. Estimation of Time Varying Variance

    • Genshiro Kitagawa, Will Gersch
    Pages 137-145

  12. Modeling Scalar Nonstationary Covariance Time Series

    • Genshiro Kitagawa, Will Gersch
    Pages 147-160

  13. Modeling Multivariate Nonstationary Covariance Time Series

    • Genshiro Kitagawa, Will Gersch
    Pages 161-179

  14. Modeling Inhomogeneous Discrete Processes

    • Genshiro Kitagawa, Will Gersch
    Pages 181-187

  15. Quasi-Periodic Process Modeling

    • Genshiro Kitagawa, Will Gersch
    Pages 189-200

  16. Nonlinear Smoothing

    • Genshiro Kitagawa, Will Gersch
    Pages 201-212

  17. Other Applications

    • Genshiro Kitagawa, Will Gersch
    Pages 213-230

  18. Back Matter

    Pages 231-263
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Keywords

About this book

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.

Authors and Affiliations

  • The Institute of Statistical Mathematics, Tokyo, Japan

    Genshiro Kitagawa

  • Department of Information and Computer Science, University of Hawaii, Honolulu, USA

    Will Gersch

Bibliographic Information

  • Book Title: Smoothness Priors Analysis of Time Series

  • Authors: Genshiro Kitagawa, Will Gersch

  • Series Title: Lecture Notes in Statistics

  • DOI: https://doi.org/10.1007/978-1-4612-0761-0

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1996

  • Softcover ISBN: 978-0-387-94819-5Published: 09 August 1996

  • eBook ISBN: 978-1-4612-0761-0Published: 06 December 2012

  • Series ISSN: 0930-0325

  • Series E-ISSN: 2197-7186

  • Edition Number: 1

  • Number of Pages: X, 280

  • Topics: Statistics, general, Analysis

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