Regression : Models, Methods and Applications /

Now in its second edition, this textbook provides an applied and unified introduction to parametric, nonparametric and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their ap...

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Bibliographic Details
Main Authors:	Fahrmeir, Ludwig (Author), Kneib, Thomas (Author), Lang, Stefan (Author), Marx, Brian D. (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2021.
Edition:	2nd ed. 2021.
Subjects:	Regression analysis. Statistics . Quantitative research. Nonparametric statistics. Mathematical statistics. Linear Models and Regression. Applied Statistics. Data Analysis and Big Data. Non-parametric Inference. Parametric Inference. Statistical Theory and Methods. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Introduction
Regression Models
The Classical Linear Model
Extensions of the Classical Linear Model
Generalized Linear Models
Categorical Regression Models
Mixed Models
Nonparametric Regression
Structured Additive Regression
Distributional Regression Models.