TY - BOOK
AU - Smithson,Michael
AU - Merkle,Edgar C.
TI - Generalized linear models for categorical and continuous limited dependent variables
T2 - Chapman & Hall/CRC statistics in the social and behavioral sciences series
SN - 9781466551732 (hardback)
U1 - 511.326 23
PY - 2014///
CY - Boca Raton
PB - CRC Press
KW - Variables (Mathematics)
KW - Linear models (Statistics)
KW - Mathematical constants
KW - MATHEMATICS / Probability & Statistics / General
N1 - Includes bibliographical references (pages 261-274) and indexes; 1. Introduction and overview--
I. Discrete variables
2. Binary variables--
3. Nominal polytomous variables--
4. Ordinal categorical variables--
5. Count variables--
II Continuous variables
6. Doubly bounded continuous variables--
7. Censoring and truncation--
8. Extensions--
References--
Author index--
Subject index
N2 - "Designed for graduate students and researchers in the behavioral, social, health, and medical sciences, this text employs generalized linear models, including mixed models, for categorical and limited dependent variables. Categorical variables include both nominal and ordinal variables. Discrete or continuous limited dependent variables have restricted support, whether through censorship or truncation or by their nature. The book incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages. "--; "This book is devoted to dependent variables other than those for which linear regression is appropriate. The authors argue that such dependent variables are, if anything, more common throughout the human sciences than the kind that suit linear regression. Presenting a broader but unified coverage in which the authors attempt to integrate concepts and ideas shared across models and types of data broader but unified coverage in which we attempt to integrate the concepts and ideas shared across models and types of data, especially regarding conceptual links between discrete and continuous limited dependent variables"--
ER -