Manual práctico de etología clínica en el perro


Manual práctico de etología clínica en el perro

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Críticas From the book reviews:“This is a very useful book for researchers in particular those often faced with data not suited to the classical linear model and for teachers who wish to motivate good students with an introduction to the wonderful and diverse world of modern statistical modeling. The use of interesting examples and well-thought-out remarks together with important theory aid the reader in getting a very good feel for the topics covered.” (Luke A. Prendergast Mathematical Reviews June 2014)“The book is an excellent resource for a wide range of readers … . more accessible to readers interested in applications of these procedures. … Summing Up: Highly recommended. Students of all levels researchers/faculty and professionals.” (D. J. Gougeon Choice Vol. 51 (8) April 2014)“This is a comprehensive review of various types of theoretical and applied regression models and methodology. … The book provides a strong mathematical base for the understanding of various types of regression models and methodology by integrating theory and practical application. … This is an excellent reference for teachers students and researchers in statistics mathematics and social economic and life sciences.” (Kamesh Sivagnanam Doody’s Book Reviews August 2013) Reseña del editor 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. Contraportada 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. Biografía 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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