Regression: Models, Methods and Applications - Softcover

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.