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Confidence Intervals in Generalized Regression Models

Autor Esa Uusipaikka
en Limba Engleză Paperback – 7 oct 2019
A Cohesive Approach to Regression ModelsConfidence 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.
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Specificații

ISBN-13: 9780367387082
ISBN-10: 0367387085
Pagini: 328
Dimensiuni: 156 x 234 mm
Greutate: 0.6 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC

Public țintă

Academic and Professional Practice & Development

Cuprins

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.

Descriere

This book introduces a unified representation—the generalized regression model—of various types of regression models, including the general linear, nonlinear regression, generalized linear, logistic regression, Poisson regression, multinomial regression, and Cox regression models. It also uses a likelihood-based approach for performing statistical inference from statistical evidence consisting of data and its statistical model. The book includes restricted versions of Mathematica® and the author’s own Statistical Inference Package (SIP) on DVD. The author also supplies the SIP and R code for several likelihood-based inference examples online.