Generalized Linear Models for Categorical and Continuous Limited Dependent Variables

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A01=Edgar C. Merkle
A01=Michael Smithson
Adjacent Categories Model
Age Group_Uncategorized
Age Group_Uncategorized
Approximate Likelihood Ratio Test
Author_Edgar C. Merkle
Author_Michael Smithson
automatic-update
Beta Regression Model
Binary Variables
Category1=Non-Fiction
Category=JMA
Category=JMB
Category=PBT
Category=PS
Censoring and Truncation
Cl Model
Continuation Ratio Model
Continuous Variables
COP=United States
Count Regression Models
Delivery_Pre-order
Deviance Residuals
Discrete Variables
eq_isMigrated=2
eq_non-fiction
eq_science
eq_society-politics
GLM Family
GSS Data
Hurdle Model
Interval Regression Model
Language_English
Min 1Q Median 3Q Max
MNL
MNL Model
MPT Model
Negative Binomial
Negative Binomial Model
Ordinal Categorical Variables
PA=Temporarily unavailable
Pearson Residual
Poisson Regression Model
Price_€100 and above
Proportional Odds Assumption
Proportional Odds Model
PS=Active
Quantile Regression
Regression Models
Roc Curve
softlaunch
Tobit

Product details

  • ISBN 9781466551732
  • Weight: 740g
  • Dimensions: 156 x 234mm
  • Publication Date: 05 Sep 2013
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages.

The book provides broad, but unified, coverage, and the authors integrate the concepts and ideas shared across models and types of data, especially regarding conceptual links between discrete and continuous limited dependent variables. The authors argue that these dependent variables are, if anything, more common throughout the human sciences than the kind that suit linear regression. They cover special cases or extensions of models, estimation methods, model diagnostics, and, of course, software. They also discuss bounded continuous variables, boundary-inflated models, and methods for modeling heteroscedasticity.

Wherever possible, the authors have illustrated concepts, models, and techniques with real or realistic datasets and demonstrations in R and Stata, and each chapter includes several exercises at the end. The illustrations and exercises help readers build conceptual understanding and fluency in using these techniques. At several points the authors bring together material that has been previously scattered across the literature in journal articles, software package documentation files, and blogs. These features help students learn to choose the appropriate models for their purpose.

Michael Smithson, Edgar C. Merkle