An Introduction to Generalized Linear Models

Häftad, Engelska, 2005

Del 145 i serien Quantitative Applications in the Social Sciences

784 kr

Beställningsvara. Skickas inom 3-6 vardagar. Fri frakt över 249 kr.

Beskrivning

Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors extend these concepts to GLM (including Poisson regression. logistic regression, and proportional hazards models) and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets, and the computer instructions and results will be presented for each example. Throughout the book, there is an emphasis on link functions and error distribution and how the model specifications translate into likelihood functions that can, through maximum likelihood estimation be used to estimate the regression parameters and their associated standard errors. This book provides readers with basic modeling principles that are applicable to a wide variety of situations.

Key Features:

- Provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation

- Includes discussion on checking model adequacy and description on how to use SAS to fit GLM

- Describes the connection between survival analysis and GLM

This book is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.

Produktinformation

Utgivningsdatum:2005-11-02
Mått:139 x 215 x 6 mm
Vikt:110 g
Format:Häftad
Språk:Engelska
Serie:Quantitative Applications in the Social Sciences
Antal sidor:88
Upplaga:1
Förlag:SAGE Publications
ISBN:9780761920847

Utforska kategorier

Sociologi inom Samhälle och politik

Mer om författaren

Prof. Ho′s research concerns with the development and application of quantitative methods in the neural and behavioral sciences. His current research interests include effective connectivity analysis in fMRI experiments, social network analysis, statistical approach for testing mathematical axioms, diagnostics in nonlinear SEM.

Innehållsförteckning

List of Figures and TablesSeries Editor’s IntroductionAcknowledgments1. Generalized Linear Models2. Some Basic Modeling ConceptsCategorical Independent VariablesEssential Components of Regression Modeling3. Classical Multiple Regression ModelAssumptions and Modeling ApproachResults of Regression AnalysisMultiple CorrelationTesting Hypotheses4. Fundamentals of Generalized Linear ModelingExponential Family of DistributionsClassical Normal RegressionLogistic RegressionPoisson RegressionProportional Hazards Survival Model5. Maximum Likelihood Estimation6. Deviance and Goodness of FitUsing Deviances to Test Statistical HypothesesGoodness of FitAssessing Goodness of Fit by Residual Analysis7. Logistic RegressionExample of Logistic Regression8. Poisson RegressionExample of Poisson Regression Model9. Survival AnalysisSurvival Time DistributionsExponential Survival ModelExample of Exponential Survival ModelConclusionsAppendixReferencesIndexAbout the Authors