Regression : Models, Methods and Applications / by Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian D. Marx.

Format:

Book

Contributor:

Fahrmeir, L., editor.

Language:

English

Subjects (All):

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.

Local Subjects:

Linear Models and Regression.

Applied Statistics.

Data Analysis and Big Data.

Non-parametric Inference.

Parametric Inference.

Statistical Theory and Methods.

Physical Description:

1 online resource (757 pages)

Edition:

2nd ed. 2021.

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2021.

Summary:

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 appropriate application is shown through numerous examples and case studies. The most important definitions and statements are concisely summarized in boxes, and the underlying data sets and code are available online on the book’s dedicated website. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. The chapters address the classical linear model and its extensions, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression and distributional regression models. Two appendices describe the required matrix algebra, as well as elements of probability calculus and statistical inference. In this substantially revised and updated new edition the overview on regression models has been extended, and now includes the relation between regression models and machine learning, additional details on statistical inference in structured additive regression models have been added and a completely reworked chapter augments the presentation of quantile regression with a comprehensive introduction to distributional regression models. Regularization approaches are now more extensively discussed in most chapters of the book. The book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written at an intermediate mathematical level and assumes only knowledge of basic probability, calculus, matrix algebra and statistics.

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.

Notes:

Includes index.

Other Format:

Print version: Fahrmeir, Ludwig Regression

ISBN:

9783662638828

3662638827