Regression

Models, Methods and Applications

Business & Finance, Economics, Statistics, Nonfiction, Science & Nature, Mathematics
Cover of the book Regression by Thomas Kneib, Brian Marx, Stefan Lang, Ludwig Fahrmeir, Springer Berlin Heidelberg
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Author: Thomas Kneib, Brian Marx, Stefan Lang, Ludwig Fahrmeir ISBN: 9783642343339
Publisher: Springer Berlin Heidelberg Publication: May 9, 2013
Imprint: Springer Language: English
Author: Thomas Kneib, Brian Marx, Stefan Lang, Ludwig Fahrmeir
ISBN: 9783642343339
Publisher: Springer Berlin Heidelberg
Publication: May 9, 2013
Imprint: Springer
Language: English

The aim of this book is an applied and unified introduction into parametric, non- 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 many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, 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 on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.

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The aim of this book is an applied and unified introduction into parametric, non- 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 many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, 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 on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.

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