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Regression Models, Methods and Applications by Thomas Kneib, Ludwig Fahrmeir HC
US $55.55
ApproximatelyRM 235.74
Condition:
“Very good condition HC, minimal marking, 698 pages.”
Very Good
A book that has been read but is in excellent condition. No obvious damage to the cover, with the dust jacket included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear.
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Located in: Scottsboro, Alabama, United States
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eBay item number:284750422438
Item specifics
- Condition
- Very Good
- Seller Notes
- “Very good condition HC, minimal marking, 698 pages.”
- ISBN
- 9783642343322
About this product
Product Identifiers
Publisher
Springer Berlin / Heidelberg
ISBN-10
3642343325
ISBN-13
9783642343322
eBay Product ID (ePID)
143884597
Product Key Features
Number of Pages
Xiv, 698 Pages
Publication Name
Regression : Models, Methods and Applications
Language
English
Publication Year
2013
Subject
Probability & Statistics / Regression Analysis, Probability & Statistics / General, Econometrics, Statistics
Type
Textbook
Subject Area
Mathematics, Business & Economics
Format
Hardcover
Dimensions
Item Height
0.6 in
Item Weight
416.9 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
Dewey Edition
23
Reviews
From the reviews: "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), 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)
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.536
Table Of 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.
Synopsis
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., 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., This book offers an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. It presents the most important models and methods on a solid formal basis and includes case studies.
LC Classification Number
QA276-280
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