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Online Public Access Catalogue

题名/责任者:

Handbook for applied modeling : non-Gaussian and correlated data / Jamie D. Riggs, Northwestern University, Illinois, Trent L. Lalonde, University of Northern Colorado

出版发行项:

Cambridge, United Kingdom ; New York, NY : Cambridge University Press, c2017

ISBN:

9781107146990 :

ISBN:

1107146992

ISBN:

9781316601051

ISBN:

1316601056

载体形态项:

xv, 216 pages : illustrations ; 26 cm

个人责任者:

Riggs, Jamie, author

附加个人名称:

Lalonde, Trent, author

论题主题:

Mathematical statistics

论题主题:

Mathematical models

论题主题:

Gaussian processes

论题主题:

Stochastic processes

中图法分类号:

O212

书目附注:

Includes bibliographical references (pages 211-212) and index

摘要附注:

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques. This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses. The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations. All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options. Common pitfalls, data issues, and interpretation of model results are also addressed. Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for their own data analysis needs. --

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