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Quantitative Applications in the Social Sciences (QASS)
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GENERALIZED LINEAR MODELS: A Unifi ed Approach SECOND EDITION, VOLUME 134
Jeff Gill, American University • Michelle Torres
This volume explains the theoretical underpinnings of generalized linear models so that researchers can decide how to select the
best way to adapt their data for this type of analysis. Examples are provided to illustrate the application of GLM to actual data.
CONTENTS 1. Introduction / 2. The Exponential Family / 3. Likelihood Theory and the Moments / 4. Linear Structure and the Link Function / 5.
Estimation Procedures / 6. Residuals and Model Fit / 7. Extensions to Generalized Linear Models / 8. Conclusion
PAPERBACK: $22.00 • ISBN: 978-1-5063-8734-5 • JUNE 2019 • 144 PAGES
PROPENSITY SCORE METHODS AND APPLICATIONS VOLUME 178
Haiyan Bai • M. H. Clark, both of University of Central Florida
Propensity Score Methods and Applications provides a concise, introductory text on propensity score methods that is
easy to comprehend by those who have limited background in statistics, and is practical enough for researchers to quickly
generalize and apply the methods.
CONTENTS 1: Basic Concepts of Propensity Score Methods / 2: Covariate Selection and Propensity Score Estimation / 3: Propensity Score Adjustment
Methods / 4: Covariate Evaluation and Causal Effect Estimation / 5: Conclusion
PAPERBACK: $22.00 • ISBN: 978-1-5063-7805-3 • ©2019 • 136 PAGES
LINEAR REGRESSION: A Mathematical Introduction VOLUME 177
Damodar N. Gujarati, West Point
Damodar N. Gujarati presents linear regression theory in a rigorous but approachable manner, so that it is accessible to
beginning graduate students in the social sciences. The technical discussion is provided in a clear and easy-to-follow style,
with advanced discussion of some of the topics offered in the appendices to the various chapters. This book is a concise
exploration of the subject, and includes end-of-chapter exercises to test mastery of the chapter content.
CONTENTS 1: The Linear Regression Model (LRM) / 2: The Classical Linear Regression Model (CLRM) / 3: The classical normal linear regression model:
The method of maximum likelihood / 4: Linear regression model: Distribution Theory and Hypothesis testing / 5: Extensions of the Classical Linear
regression model: generalized least squares (GLS) / 6: Extensions of the Classical linear regression model: the case of stochastic or endogenous regressors
/ 7: Selected Topics in Linear Regression
PAPERBACK: $22.00 • ISBN: 978-1-5443-3657-2 • ©2019 • 272 PAGES
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