Confidence Intervals in Generalized Regression Models (Statistics: A Series of Textbooks and Monographs)
By: Esa Uusipaikka (author)Hardback
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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.
Introduction Likelihood-Based Statistical Inference Statistical evidence Statistical inference Likelihood concepts and law of likelihood Likelihood-based methods Profile likelihood-based confidence intervals Likelihood ratio tests (LRTs) Maximum likelihood estimate (MLE) Model selection Generalized Regression Model Examples of regression data Definition of generalized regression models (GRMs) Special cases of GRM Likelihood inference MLE with iterative reweighted least squares Model checking General Linear Model Definition of the general linear model (GLM) Estimate of regression coefficients Test of linear hypotheses Confidence regions and intervals Model checking Nonlinear Regression Model Definition of the nonlinear regression model Estimate of regression parameters Approximate distribution of LRT statistic Profile likelihood-based confidence region Profile likelihood-based confidence interval LRT for a hypothesis on finite set of functions Model checking Generalized Linear Model Definition of generalized linear model (GLIM) MLE of regression coefficients Binomial and Logistic Regression Models Data Binomial distribution Link functions Likelihood inference Logistic regression model Models with other link functions Nonlinear binomial regression model Poisson Regression Model Data Poisson distribution Link functions Likelihood inference Log-linear model Multinomial Regression Data Multinomial distribution Likelihood function Logistic multinomial regression model Proportional odds regression model Other Generalized Linear Regressions Models Negative binomial regression model Gamma regression model Other Generalized Regression Models Weighted GLM Weighted nonlinear regression model Quality design or Taguchi model Lifetime regression model Cox regression model Appendix A: Data Sets Appendix B: Notation Used for Statistical Models Bibliographic notes appear at the end of each chapter.
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- ID: 9781420060270
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