Bayesian Analysis of Time Series, Lyle D. Broemeling

Автор: Hamilton, James
Название: Time Series Analysis
ISBN: 0691042896 ISBN-13(EAN): 9780691042893
Издательство: Wiley
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Описание: A graduate-level text which describes the recent dramatic changes that have taken place in the way that researchers analyze economic and financial time series. It explores such important innovations as vector regression, nonlinear time series models and the generalized methods of moments.

Автор: Arnold Zellner (Editor)
Название: The Structural Econometric Time Series Analysis Approach
ISBN: 0521187435 ISBN-13(EAN): 9780521187435
Издательство: Cambridge Academ
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Цена: 7443.00 р.
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Описание: This book assembles previously published texts in the theory and application of the Structural Econometric Time Series Analysis (SEMTSA) approach. It provides a discussion of major considerations relating to the construction of econometric models that work well to explain economic phenomena, predict future outcomes and be useful for policy-making.

Автор: Box-Steffensmeier
Название: Time Series Analysis for the Social Sciences
ISBN: 0521691559 ISBN-13(EAN): 9780521691550
Издательство: Cambridge Academ
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Цена: 4592.00 р.
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Описание: Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. The book covers ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting.

Автор: Gregory
Название: Bayesian Logical Data Analysis for the Physical Sciences
ISBN: 0521150124 ISBN-13(EAN): 9780521150125
Издательство: Cambridge Academ
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Описание: Increasingly, researchers in many branches of science are coming into contact with Bayesian statistics or Bayesian probability theory. This book provides a clear exposition of the underlying concepts with large numbers of worked examples and problem sets. Background material is provided in appendices and supporting Mathematica (R) notebooks are available.

Автор: Muller, P., Quintana, F.A., Jara, A., Hanson, T.
Название: Bayesian Nonparametric Data Analysis
ISBN: 3319189670 ISBN-13(EAN): 9783319189673
Издательство: Springer
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Цена: 11878.00 р.
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Описание: This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones.

Автор: Commandeur, Jacques J.F.; Koopman, Siem Jan
Название: An Introduction to State Space Time Series Analysis
ISBN: 0199228876 ISBN-13(EAN): 9780199228874
Издательство: Oxford Academ
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Цена: 7681.00 р.
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Описание: This text provides an introduction to time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. This is the first in a series of books designed to provide practitioners, researchers, and students with practical introductions to various topics in econometrics.

Автор: Matthias Kaeding
Название: Bayesian Analysis of Failure Time Data Using P-Splines
ISBN: 3658083921 ISBN-13(EAN): 9783658083922
Издательство: Springer
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Цена: 9141.00 р.
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Описание: Matthias Kaeding discusses Bayesian methods for analyzing discrete and continuous failure times where the effect of time and/or covariates is modeled via P-splines and additional basic function expansions, allowing the replacement of linear effects by more general functions.

Автор: Gelman
Название: Bayesian Data Analysis, Third Edition
ISBN: 1439840954 ISBN-13(EAN): 9781439840955
Издательство: Taylor&Francis
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Описание: Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.