The book brings together material on the analysis of limited and bounded variables that is scattered across the literature in several disciplines, and presents it in a style that is both more accessible and up-to-date. The authors provide worked examples in each chapter using real datasets from a variety of disciplines. The software used for the examples include R, SAS, and Stata. The data, software code, and detailed explanations of the example models are available on an accompanying website.

Michael Smithson is a Professor in the Research School of Psychology at The Australian National University in Canberra, and received his PhD from the University of Oregon. He is the author of Confidence Intervals (2003), Statistics with Confidence (2000), Ignorance and Uncertainty (1989), and Fuzzy Set Analysis for the Behavioral and Social Sciences (1987), co-author of Fuzzy Set Theory: Applications in the Social Sciences (2006) and Generalized Linear Models for Categorical and Limited Dependent Variables (2014), and co-editor of Uncertainty and Risk: Multidisciplinary Perspectives (2008) and Resolving Social Dilemmas: Dynamic, Structural, and Intergroup Aspects (1999). His other publications include more than 170 refereed journal articles and book chapters. His primary research interests are in judgment and decision making under ignorance and uncertainty, statistical methods for the social sciences, and applications of fuzzy set theory to the social sciences.

1. Introduction and Overview
Overview of this Book
The Nature of Bounds on Variables
The Generalized Linear Model
Examples
2. Models for Singly-Bounded Variables
GLMs for singly-bounded variables
Model Diagnostics
Treatment of Boundary Cases
3. Models for Doubly-Bounded Variables
Doubly-Bounded Variables and \Natural" Heteroskedasticity
The Beta Distribution: Definition and Properties
Modeling Location and Dispersion
Estimation and Model Diagnostics
Treatment of Cases at the Boundaries
4. Quantile Models for Bounded Variables
Introduction
Quantile regression
Distributions for Doubly-Bounded Variables with Explicit Quantile Functions
The CDF-Quantile GLM
5. Censored and Truncated Variables
Types of censoring and truncation
Tobit models
Tobit Model Example
Heteroskedastic and Non-Gaussian Tobit Models
6. Extensions and Conclusions
Extensions and a General Framework
Absolute Bounds and Censoring
Multi-Level and Multivariate Models
Bayesian Estimation and Modeling
Roads Less Traveled and the State of the Art
References