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    Introduction to Applied Bayesian Statistics and Estimation for Social Scientists (Statistics for Social and Behavioral Sciences) (Hardback) By (author) Scott M. Lynch

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    DescriptionThis book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.


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  • Full bibliographic data for Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

    Title
    Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
    Authors and contributors
    By (author) Scott M. Lynch
    Physical properties
    Format: Hardback
    Number of pages: 387
    Width: 156 mm
    Height: 234 mm
    Thickness: 22 mm
    Weight: 1,590 g
    Language
    English
    ISBN
    ISBN 13: 9780387712642
    ISBN 10: 038771264X
    Classifications

    BIC E4L: MAT
    Nielsen BookScan Product Class 3: S7.8
    B&T Book Type: NF
    B&T Merchandise Category: TXT
    B&T Modifier: Region of Publication: 01
    LC subject heading:
    B&T General Subject: 750
    LC subject heading:
    BIC subject category V2: PBT, JH
    Ingram Subject Code: SO
    Warengruppen-Systematik des deutschen Buchhandels: 17440
    B&T Modifier: Academic Level: 02
    B&T Modifier: Text Format: 06
    LC subject heading:
    BISAC V2.8: SOC019000
    LC subject heading: ,
    BISAC V2.8: MAT003000
    LC subject heading:
    BISAC V2.8: SOC027000
    DC22: 300.727
    LC subject heading:
    BISAC V2.8: MAT029010
    Libri: STAT4000, SOZJ2000, WISS3074, BEVO5022, DEMO1100
    DC22: 300.1/519542
    LC classification: HA29 .L973 2007
    DC22: 300.1519542
    LC classification: H1-970.9, QA276-280, H61-61.95
    LC subject heading:
    LC classification: HB848-3697
    Thema V1.0: PBT, PBW, JH
    Illustrations note
    biography
    Publisher
    Springer-Verlag New York Inc.
    Imprint name
    Springer-Verlag New York Inc.
    Publication date
    15 August 2007
    Publication City/Country
    New York, NY
    Review quote
    From the reviews: "The book ... contains a very detailed and comprehensive description of MCMC methods useful for applied researchers. ... Undoubtedly the book is interesting ... . The reader will gain an extensive knowledge of the issues covered ... ." (Dimitris Karlis, Zentralblatt MATH, Vol. 1133 (11), 2008) "This new offering adds to our burgeoning Bayesian bookshelves a text directed at social scientists ... . To summarize, this a very useful text for a tightly bounded semester-long introduction to Bayesian statistics in the social sciences. The text is distinguished by its hands-on practical orientation which many readers will find very appealing. ... In addition, the book is handy for self-study ... ." (Jeff Gill, Journal of the American Statistical Association, Vol. 103 (483), September, 2008) "This book introduces readers to the world of Bayesian analysis and MCMC methods through brief discussions of theory, examples, and programming computations for pplications. ...The potential users of the book are students or researchers in the social sciences, or anyone that is interested in learning Bayesian techniques and MCMC methods and applying them to their practice. The book is geared... towards practical applications. ... I recommended this book to anyone who is interested in learning about Bayesian inference and MCMC methods." (Journal of Educational Measurement . Summer 2010, Vol. 47, No 2, pp. 250-254)
    Back cover copy
    Introduction to Applied Bayesian Statistics and Estimation for Social Scientists covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research, including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models, and it thoroughly develops each real-data example in painstaking detail. The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods including the Gibbs sampler and the Metropolis-Hastings algorithm are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data. Scott M. Lynch is an associate professor in the Department of Sociology and Office of Population Research at Princeton University. His substantive research interests are in changes in racial and socioeconomic inequalities in health and mortality across age and time. His methodological interests are in the use of Bayesian stastistics in sociology and demography generally and in multistate life table methodology specifically."
    Table of contents
    Probability Theory and Classical Statistics.- Basics of Bayesian Statistics.- Modern Model Estimation Part 1: Gibbs Sampling.- Modern Model Estimation Part 2: Metroplis-Hastings Sampling.- Evaluating Markov Chain Monte Carlo Algorithms and Model Fit.- The Linear Regression Model.- Generalized Linear Models.- to Hierarchical Models.- to Multivariate Regression Models.- Conclusion.