Confidence intervals in generalized regression models. With CD-ROM. Regression models are used to predict one variable from one or more other variables. Their goal is to create a model such that the predicted and observed values of the outcome variable are as similar as possible. This book presents well-known regression models (linear and non-linear models, binomial and logistic models, Poisson and multinomial models and other generalized regression models). The book follows two directions: profile likelihood-based confidence intervals and generalized regression models. It is intended for senior undergraduate and graduate students (generalized regression models) and for applied statisticians too (confidence intervals). Besides the textbook, a DVD with a restricted Mathematica version and the author’s own Statistical Inference Package (SIP) is also included. As the author explains, one of the aims of this book is to introduce a unified representation of different regression models, under the name generalized (parametric) regression models (GRM). Logically, the second aim of the book is to use a unified approach for statistical inference from statistical evidence consisting of data and its statistical models, under the name of likelihood-based approach. To conclude, the text and the corresponding software focus on producing statistical inference for data modeled by GRMs.
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References in zbMATH (referenced in 2 articles , 1 standard article )
Showing results 1 to 2 of 2.
- Withers, Christopher S.; Nadarajah, Saralees: Improved confidence regions based on Edgeworth expansions (2012)
- Uusipaikka, Esa: Confidence intervals in generalized regression models. With CD-ROM (2009)