All listings for this product
Best-selling in Non-Fiction Books
Save on Non-Fiction Books
- AU $9.86Trending at AU $16.86
- AU $11.51Trending at AU $18.84
- AU $24.98Trending at AU $25.26
- AU $64.07Trending at AU $75.02
- AU $28.70Trending at AU $37.03
- AU $44.96Trending at AU $47.20
- AU $24.92Trending at AU $28.26
About this product
- DescriptionThe aim of this book is an applied and unified introduction into parametric, n- 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, ecomic, 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 kwledge 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.
- Author BiographyLudwig 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 Gottingen, 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 Gottingen. 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.
- Author(s)Brian Marx,Ludwig Fahrmeir,Stefan Lang,Thomas Kneib
- PublisherSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
- Date of Publication09/05/2013
- SubjectMedicine: General
- Place of PublicationBerlin
- Country of PublicationGermany
- ImprintSpringer-Verlag Berlin and Heidelberg GmbH & Co. K
- Content Note204 black & white illustrations, biography
- Weight1226 g
- Width156 mm
- Height234 mm
- Spine38 mm
- Format DetailsLaminated cover
This item doesn't belong on this page.
Thanks, we'll look into this.