Confidence Intervals in Generalized Regression Models: 1st Edition (Paperback) book cover

Confidence Intervals in Generalized Regression Models

1st Edition

By Esa Uusipaikka

Chapman and Hall/CRC

328 pages

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A Cohesive Approach to Regression Models

Confidence Intervals in Generalized Regression Models introduces a unified representation—the generalized regression model (GRM)—of various types of regression models. It also uses a likelihood-based approach for performing statistical inference from statistical evidence consisting of data and its statistical model.

Provides a Large Collection of Models

The book encompasses a number of different regression models, from very simple to more complex ones. It covers the general linear model (GLM), nonlinear regression model, generalized linear model (GLIM), logistic regression model, Poisson regression model, multinomial regression model, and Cox regression model. The author also explains methods of constructing confidence regions, profile likelihood-based confidence intervals, and likelihood ratio tests.

Uses Statistical Inference Package to Make Inferences on Real-Valued Parameter Functions

Offering software that helps with statistical analyses, this book focuses on producing statistical inferences for data modeled by GRMs. It contains numerical and graphical results while providing the code online.

Table of Contents

Introduction. Likelihood-Based Statistical Inference. Generalized Regression Model.General Linear Model.Nonlinear Regression Model. Generalized Linear Model.Binomial and Logistic Regression Models.Poisson Regression Model.Multinomial Regression.Other Generalized Linear Regressions Models.Other Generalized Regression Models. Appendices.

About the Author

Uusipaikka, Esa

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General