# Logistic Regression Models

## Hilbe, J.

1ª Edición Mayo 2009

Inglés

Tapa dura

656 pags

1500 gr

16 x 24 x 3 cm

### ISBN 9781420075755

### Editorial CRC PRESS

Recíbelo en un plazo De 2 a 3 semanas

**Summary
**

Logistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The text illustrates how to apply the various models to health, environmental, physical, and social science data.

Many examples help explain the concepts and techniques of successful modeling

The text first provides basic terminology and concepts, before explaining the
foremost methods of estimation (maximum likelihood and IRLS) appropriate for
logistic models. It then presents an in-depth discussion of related terminology
and examines logistic regression model development and interpretation of the
results. After focusing on the construction and interpretation of various interactions,
the author evaluates assumptions and goodness-of-fit tests that can be used
for model assessment. He also covers binomial logistic regression, varieties
of overdispersion, and a number of extensions to the basic binary and binomial
logistic model. Both real and simulated data are used to explain and test the
concepts involved. The appendices give an overview of marginal effects and discrete
change as well as a 30-page tutorial on using Stata commands related to the
examples used in the text. Stata is used for most examples while R is provided
at the end of the chapters to replicate examples in the text.

Apply the models to your own data

Data files for examples and questions used in the text as well as code for user-authored
commands are provided on the book’s website, formatted in Stata, R, Excel,
SAS, SPSS, and Limdep.

**Features**

• Examines the theoretical foundation of many logistic models, including
binary, ordered, multinomial, panel, and exact

• Describes how each type of model is established, interpreted, and evaluated
as to its goodness of fit

• Analyzes the models using Stata

• Offers R code at the end of most chapters to enable R users to duplicate
the output displayed in the text

• Includes numerous exercises and real-world examples from the medical,
ecological, physical, and social sciences

• Provides the example data sets online in Stata, R, Excel, SAS, SPSS,
and Limdep formats

Solutions manual available upon qualifying course adoptions

**Table of Contents**

Preface

Introduction

The Normal Model

Foundation of the Binomial Model

Historical and Software Considerations

Chapter Profiles

**Concepts Related to the Logistic Model **

2 × 2 Table Logistic Model

2 × k Table Logistic Model

Modeling a Quantitative Predictor

Logistic Modeling Designs

**Estimation Methods **

Derivation of the IRLS Algorithm

IRLS Estimation

Maximum Likelihood Estimation

**Derivation of the Binary Logistic Algorithm **

Terms of the Algorithm

Logistic GLM and ML Algorithms

Other Bernoulli Models

**Model Development **

Building a Logistic Model

Assessing Model Fit: Link Specification

Standardized Coefficients

Standard Errors

Odds Ratios as Approximations of Risk Ratios

Scaling of Standard Errors

Robust Variance Estimators

Bootstrapped and Jackknifed Standard Errors

Stepwise Methods

Handling Missing Values

Modeling an Uncertain Response

Constraining Coefficients

**Interactions**

Introduction

Binary X Binary Interactions

Binary X Categorical Interactions

Binary X Continuous Interactions

Categorical X Continuous Interaction

Thoughts about Interactions

**Analysis of Model Fit **

Traditional Fit Tests for Logistic Regression

Hosmer–Lemeshow GOF Test

Information Criteria Tests

Residual Analysis

Validation Models

**Binomial Logistic Regression
Overdispersion **

Introduction

The Nature and Scope of Overdispersion

Binomial Overdispersion

Binary Overdispersion

Real Overdispersion

Concluding Remarks

**Ordered Logistic Regression**

Introduction

The Proportional Odds Model

Generalized Ordinal Logistic Regression

Partial Proportional Odds

**Multinomial Logistic Regression**Unordered Logistic Regression

Independence of Irrelevant Alternatives

Comparison to Multinomial Probit

**Alternative Categorical Response Models**Introduction

Continuation Ratio Models

Stereotype Logistic Model

Heterogeneous Choice Logistic Model

Adjacent Category Logistic Model

Proportional Slopes Models

**Panel Models**Introduction

Generalized Estimating Equations

Unconditional Fixed Effects Logistic Model

Conditional Logistic Models

Random Effects and Mixed Models Logistic Regression

**Other Types of Logistic-Based Models**Survey Logistic Models

Scobit-Skewed Logistic Regression

Discriminant Analysis

**Exact Logistic Regression**Exact Methods

Alternative Modeling Methods

**Conclusion**Appendix A: Brief Guide to Using Stata Commands

Appendix B: Stata and R Logistic Models

Appendix C: Greek Letters and Major Functions

Appendix D: Stata Binary Logistic Command

Appendix E: Derivation of the Beta-Binomial

Appendix F: Likelihood Function of the Adaptive Gauss–Hermite Quadrature Method of Estimation

Appendix G: Data Sets

Appendix H: Marginal Effects and Discrete Change

References

Author Index

Subject Index

Exercises and R Code appear at the end of most chapters.

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