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Regression : models, methods and applications / Ludwig Fahrmeir... [et al.]

Coauthor Fahrmeir, L. Country Alemanha. Publication Heidelberg : Springer, cop. 2013 Description XIV, 896 p. : il., gráficos ; 25 cm ISBN 978-3-642-34332-2 CDU 519.233.5
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Monografia Biblioteca Geral da Universidade do Minho
BGUM 519.233.5 - R Available 443531

Mestrado em Estatística para Ciência de Dados Modelos Lineares 1º semestre

Mestrado em Estatística para Ciência de Dados Modelos Lineares Generalizados e Aplicações 2º semestre

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Enhanced descriptions from Syndetics:

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.

Reviews provided by Syndetics


Based partially on an earlier German work, this translated version has been improved and written in a more detailed, updated, and expanded format. Fahrmeir (Univ. of Munich, Germany) and colleagues provide a seamless narrative blending theory and application in both parametric and nonparametric regression. Using case studies and in many instances real-world examples, the book is an excellent resource for a wide range of readers, from students to researchers and teachers in statistics, mathematics, and computer science. Many current books on regression deal either exclusively with linear regression or with more complex, higher-level nonparametric and semiparametric methods. Here, the authors bridge this gap by making this text more accessible to readers interested in applications of these procedures. Through the use of symbols and comprehensive appendixes, they are also able to guide independent readers. Some of the topics covered in the book's ten chapters include linear models, quantile regression, frequentist and Bayesian approaches to regression, and structured additive regression. A knowledge of basic probability, calculus, and statistics (intermediate level) is required to follow the text. Summing Up: Highly recommended. Students of all levels, researchers/faculty, and professionals. D. J. Gougeon University of Scranton

Author notes provided by Syndetics

Ludwig Fahrmeir is Professor emeritus at the Department of Statistics at Ludwig-Maximilians-University Munich. From 1995 to 2006 he was speaker of the Collaborative Research Center 'Statistical Analysis of Discrete Data', supported financially by the German National Science Foundation. His main research interests are semiparametric regression, longitudinal data analysis and spatial statistics, with applications ranging from social science and risk management to public health and neuroscience.

Thomas Kneib is Professor for Statistics at Georg August University Göttingen, Germany, where he is speaker of the interdisciplinary Centre for Statistics and a Research Training Group on "Scaling Problems in Statistics". He received his PhD in Statistics at Ludwig-Maximilians-University Munich and, during his PostDoc phase, has been Visiting Professor for Applied Statistics at the University of Ulm and Substitute Professor for Statistics at Georg-August-University Göttingen. From 2009 until 2011 he has been Professor for Applied Statistics at Carl von Ossietzky University Oldenburg. His main research interests include semiparametric regression, spatial statistics and quantile regression.

Stefan Lang is Professor for Applied Statistics at University of Innsbruck, Austria. He received his PhD at Ludwig-Maximilians-University Munich. From 2005 to 2006 he has been Professor for Statistics at University of Leipzig. He is currently editor of Advances of Statistical Analysis and Associate Editor of Statistical Modelling. His main research interests include semiparametric and spatial regression, multilevel modelling and complex Bayesian models, with applications among others in environmetrics, marketing science, real estate and actuarial science.

Brian D. Marx is a full professor in the Department of Experimental Statisitics at Louisiana State University. His main research interests include P-spline smoothiing, ill-conditioned regression problems, and high-dimensional chemometric applications. He is currently serving as coordinating editor for the journal Statistical Modelling and is past chair of the Statistical Modelling Society.

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