| Description |
xxiii, 284 pages ; 25 cm |
| Series |
Chapman & Hall/CRC statistics in the social and behavioral sciences series |
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Statistics in the social and behavioral sciences series.
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| Contents |
Contents note continued: 3.3.Multinomial Processing Tree Models -- 3.4.Estimation Methods and Model Evaluation -- 3.4.1.Estimation Methods and Model Comparison -- 3.4.2.Model Evaluation and Diagnosis -- 3.5.Analyses in R and Stata -- 3.5.1.Analyses in R -- 3.5.2.Analyses in Stata -- 3.6.Exercises -- 4.Ordinal Categorical Variables -- 4.1.Modeling Ordinal Variables: Common Practice versus Best Practice -- 4.2.Ordinal Model Alternatives -- 4.2.1.The Proportional Odds Assumption -- 4.2.2.Modeling Relative Probabilities -- 4.3.Cumulative Models -- 4.3.1.The Proportional Odds Model -- 4.3.2.Example -- 4.4.Adjacent Models -- 4.4.1.The Adjacent Categories Model -- 4.4.2.Example -- 4.5.Stage Models -- 4.5.1.The Continuation Ratio Model -- 4.5.2.Example -- 4.6.Estimation Methods and Issues -- 4.6.1.Model Choice -- 4.6.2.Model Diagnostics -- 4.7.Analyses in R and Stata -- 4.7.1.Analyses in R -- 4.7.2.Analyses in Stata -- 4.8.Exercises -- 5.Count Variables -- |
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Contents note continued: 5.1.Distributions for Count Data -- 5.2.Poisson Regression Models -- 5.2.1.Model Definition -- 5.2.2.Example -- 5.2.3.Exposure -- 5.2.4.Overdispersion and Quasi-Poisson Models -- 5.3.Negative Binomial Models -- 5.3.1.Model Definition -- 5.3.2.Example -- 5.4.Truncated and Censored Models -- 5.5.Zero-Inflated and Hurdle Models -- 5.5.1.Hurdle Models -- 5.5.2.Zero-Inflated Models -- 5.6.Estimation Methods and Issues -- 5.6.1.Negative Binomial Model Estimation -- 5.6.2.Model Diagnostics -- 5.7.Analyses in R and Stata -- 5.7.1.Analyses in R -- 5.7.2.Analyses in Stata -- 5.8.Exercises -- II.Continuous Variables -- 6.Doubly Bounded Continuous Variables -- 6.1.Doubly Bounded versus Censored -- 6.2.The beta GLM -- 6.3.Modeling Location and Dispersion -- 6.3.1.Judged Probability of Guilt -- 6.3.2.Reading Accuracy for Dyslexic and Non-Dyslexic Readers -- 6.3.3.Model Comparison -- 6.4.Estimation Methods and Issues -- 6.4.1.Estimator Bias -- 6.4.2.Model Diagnostics -- |
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Contents note continued: 6.5.Zero- and One-Inflated Models -- 6.6.Finite Mixture Models -- 6.6.1.Car Dealership Example -- 6.7.Analyses in R and Stata -- 6.7.1.Analyses in R -- 6.7.2.Analyses in Stata -- 6.8.Exercises -- 7.Censoring and Truncation -- 7.1.Models for Censored and Truncated Variables -- 7.1.1.Tobit Models -- 7.2.Non-Gaussian Censored Regression -- 7.3.Estimation Methods, Model Comparison, and Diagnostics -- 7.4.Extensions of Censored Regression Models -- 7.4.1.Proportional Hazard and Proportional Odds Models -- 7.4.2.Double and Interval Censoring -- 7.4.3.Censored Quantile Regression -- 7.5.Analyses in R and Stata -- 7.5.1.Analyses in R -- 7.5.2.Analyses in Stata -- 7.6.Exercises -- 8.Extensions -- 8.1.Extensions and Generalizations -- 8.2.Multilevel Models -- 8.2.1.Multilevel Binary Logistic Regression -- 8.2.2.Multilevel Count Models -- 8.2.3.Multilevel Beta Regression -- 8.3.Bayesian Estimation -- 8.3.1.Bayesian Binomial GLM -- 8.3.2.Bayesian Beta Regression -- |
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Contents note continued: 8.3.3.Modeling Random Sums -- 8.4.Evaluating Relative Importance of Predictors in GLMs |
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Machine generated contents note: 1.Introduction and Overview -- 1.1.The Nature of Limited Dependent Variables -- 1.2.Overview of GLMs -- 1.2.1.Definition -- 1.2.2.Extensions -- 1.3.Estimation Methods and Model Evaluation -- 1.3.1.Model Evaluation and Diagnosis -- 1.3.2.Model Selection and Interpretation Issues -- 1.4.Organization of This Book -- I.Discrete Variables -- 2.Binary Variables -- 2.1.Logistic Regression -- 2.2.The Binomial GLM -- 2.2.1.Latent Variable Interpretation -- 2.2.2.Interpretation of Coefficients -- 2.2.3.Example -- 2.2.4.Extension to n > 1 -- 2.2.5.Alternative Link Functions -- 2.3.Estimation Methods and Issues -- 2.3.1.Model Evaluation and Diagnostics -- 2.3.2.Overdispersion -- 2.3.3.Relationships to Other Models -- 2.4.Analyses in R and Stata -- 2.4.1.Analyses in R -- 2.4.2.Analyses in Stata -- 2.5.Exercises -- 3.Nominal Polytomous Variables -- 3.1.Multinomial Logit Model -- 3.2.Conditional Logit and Choice Models -- |
| Summary |
"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. "-- |
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"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"-- |
| Analysis |
Australian |
| Notes |
A Chapman and Hall book |
| Bibliography |
Includes bibliographical references and indexes |
| Subject |
Linear models (Statistics)
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Mathematical constants.
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Mathematics.
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Variables (Mathematics)
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| Author |
Merkle, Edgar C., 1978- author
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| LC no. |
2013016237 |
| ISBN |
1466551739 (hardback) |
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9781466551732 (hardback) |
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