- Membership
- Publications
- Meetings
- Competitions
- Community
- Programs
- Students
- High School Teachers
- Faculty and Departments
- Underrepresented Groups
- MAA Awards
- MAA Grants

- News
- About MAA

Publisher:

Chapman & Hall/CRC

Publication Date:

2011

Number of Pages:

280

Format:

Hardcover

Price:

89.95

ISBN:

9781439813263

Category:

Monograph

We do not plan to review this book.

**Introduction **

**Generalized Linear Models for Continuous/Interval Scale Data **Introduction

Continuous/interval scale data

Simple and multiple linear regression models

Checking assumptions in linear regression models

Likelihood: multiple linear regression

Comparing model likelihoods

Application of a multiple linear regression model

**Generalized Linear Models for Other Types of Data **Binary data

Ordinal data

Count data

**Family of Generalized Linear Models **Introduction

The linear model

Binary response models

Poisson model

Likelihood

**Mixed Models for Continuous/Interval Scale Data **Introduction

Linear mixed model

The intraclass correlation coefficient

Parameter estimation by maximum likelihood

Regression with level-two effects

Two-level random intercept models

General two-level models including random intercepts

Likelihood

Residuals

Checking assumptions in mixed models

Comparing model likelihoods

Application of a two-level linear model

Two-level growth models

Likelihood

Example on linear growth models

**Mixed Models for Binary Data **Introduction

The two-level logistic model

General two-level logistic models

Intraclass correlation coefficient

Likelihood

Example on binary data

**Mixed Models for Ordinal Data **Introduction

The two-level ordered logit model

Likelihood

Example on mixed models for ordered data

**Mixed Models for Count Data **Introduction

The two-level Poisson model

Likelihood

Example on mixed models for count data

**Family of Two-Level Generalized Linear Models **Introduction

The mixed linear model

Mixed binary response models

Mixed Poisson model

Likelihood

**Three-Level Generalized Linear Models **Introduction

Three-level random intercept models

Three-level generalized linear models

Linear models

Binary response models

Likelihood

Example on three-level generalized linear models

**Models for Multivariate Data **Introduction

Multivariate two-level generalized linear model

Bivariate Poisson model: Example

Bivariate ordered response model: Example

Bivariate linear-probit model: Example

Multivariate two-level generalized linear model likelihood

**Models for Duration and Event History Data **Introduction

Duration data in discrete time

Renewal data

Competing risk data

**Stayers, Non-Susceptibles, and Endpoints **Introduction

Mover-stayer model

Likelihood with mover-stayer model

Example 1: Stayers in Poisson data

Example 2: Stayers in binary data

**Handling Initial Conditions/State Dependence in Binary Data **Introduction to key issues: heterogeneity, state dependence and non-stationarity

Motivational example

Random effects model

Initial conditions problem

Initial treatment of initial conditions problem

Example: Depression data

Classical conditional analysis

Classical conditional model: Depression example

Conditioning on initial response but allowing random effect

Wooldridge conditional model: Depression example

Modeling the initial conditions

Same random effect in the initial response and subsequent response models with a common scale parameter

Joint analysis with a common random effect: Depression example

Same random effect in models of the initial response and subsequent responses but with different scale parameters

Joint analysis with a common random effect (different scale parameters): Depression example

Different random effects in models of the initial response and subsequent responses

Different random effects: Depression example

Embedding the Wooldridge approach in joint models for the initial response and subsequent responses

Joint model plus the Wooldridge approach: Depression example

Other link functions

**Incidental Parameters: An Empirical Comparison of Fixed Effects and Random Effects Models**Introduction

Fixed effects treatment of the two-level linear model

Dummy variable specification of the fixed effects model

Empirical comparison of two-level fixed effects and random effects estimators

Implicit fixed effects estimator

Random effects models

Comparing two-level fixed effects and random effects models

Fixed effects treatment of the three-level linear model

**Appendix A: SabreR Installation, SabreR Commands, Quadrature, Estimation, Endogenous EffectsAppendix B: Introduction to R for Sabre**

** **

**Bibliography**

- Log in to post comments