Regression : Models, Methods and Applications
Marx, Brian, Fahrmeir, Ludwig, Lang, Stefan, Kneib, Thomas
ISBN 10: 3642343325 ISBN 13: 9783642343322
Editorial: Springer Berlin / Heidelberg, 2013
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Descripción
Descripción:
Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects. N° de ref. del artículo 38930326-20
Sinopsis:
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.
Acerca del autor:
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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Detalles bibliográficos
Título: Regression : Models, Methods and Applications
Editorial: Springer Berlin / Heidelberg
Año de publicación: 2013
Encuadernación: Encuadernación de tapa dura
Condición: Very Good
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Regression: Models, Methods and Applications [Englisch] [Gebundene Ausgabe] Mathematik Informatik Mathematik Wirtschaft Lexika Generalized Linear Models linear regression mixed models Regression Statistik Semiparametric Regression spatial regression Wirtschaftsstatistik Ludwig Fahrmeir (Autor), Thomas Kneib (Autor), Stefan Lang (Autor), Brian Marx (Autor)
Librería: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, Alemania
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Hardcover. Condición: gut. 2013. 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. Autor: 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. Content: 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.- Quantile Regression.- A Matrix Algebra.- B Probability Calculus and Statistical Inference.- Bibliography.- Index. Zusatzinfo XIV, 698 p. Verlagsort Berlin Sprache englisch Maße 155 x 235 mm Mathematik / Informatik Mathematik Wirtschaft Lexika Generalized Linear Models linear regression mixed models Regression Statistik Semiparametric Regression spatial regression Wirtschaftsstatistik ISBN-10 3-642-34332-5 / 3642343325 ISBN-13 978-3-642-34332-2 / 9783642343322 In englischer Sprache. 650 pages. 15,6 x 3,8 x 23,4 cm. Nº de ref. del artículo: BN25161
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Regression
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Springer Berlin Heidelberg, 2013
ISBN 10: 3642343325
ISBN 13: 9783642343322
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and applicationWritten in textbook style suitable for students, the material is close to current research on advanced regressio. Nº de ref. del artículo: 5057577
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Regression: Models, Methods and Applications [Hardcover] Fahrmeir, Ludwig; Kneib, Thomas; Lang, Stefan and Marx, Brian
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Regression
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 698 pp. Englisch. Nº de ref. del artículo: 9783642343322
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783642343322
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