Confidence Intervals in Generalized Regression Models
Auteur : Uusipaikka Esa
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.
Date de parution : 10-2019
15.6x23.4 cm
Date de parution : 07-2008
Ouvrage de 500 p.
15.6x23.4 cm
Thème de Confidence Intervals in Generalized Regression Models :
Mots-clés :
Log Likelihood Function; Interest Function; Cumulative Distribution Function; Regression Model; Link Function; Observed Log Likelihood Function; Fuel Consumption Data; GLM; Yi Ln; Scatter Plot; Model Matrix; Response Vector; GRM; Nonlinear Regression Model; Regression Parameters; Regression Parameter Vector; Generalized Regression Models; Statistically Independent; Likelihood Ratio Statistic; Common Success Probability; Denominator Degrees; Maximum Likelihood; Multinomial Regression Model; Perch Height; Parent Smoking Behavior