Handbook of Regression Methods
Autor Derek Scott Youngen Limba Engleză Hardback – 5 iul 2017
This handbook covers classic material about simple linear regression and multiple linear regression, including assumptions, effective visualizations, and inference procedures. It presents an overview of advanced diagnostic tests, remedial strategies, and model selection procedures. Finally, many chapters are devoted to a diverse range of topics, including censored regression, nonlinear regression, generalized linear models, and semiparametric regression.
Features
- Presents a concise overview of a wide range of regression topics not usually covered in a single text
- Includes over 80 examples using nearly 70 real datasets, with results obtained using R
- Offers a Shiny app containing all examples, thus allowing access to the source code and the ability to interact with the analyses
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Specificații
ISBN-13: 9781498775298
ISBN-10: 1498775292
Pagini: 654
Ilustrații: 50 Illustrations, black and white
Dimensiuni: 156 x 234 x 42 mm
Greutate: 0.76 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
ISBN-10: 1498775292
Pagini: 654
Ilustrații: 50 Illustrations, black and white
Dimensiuni: 156 x 234 x 42 mm
Greutate: 0.76 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Cuprins
Introduction. Simple Linear Regression. The Basics of Regression Models. Statistical Inference. Statistical Intervals. Assessing Regression Assumptions. ANOVA I. Multiple Linear Regression. Multiple Regression. Matrix Notation in Regression. Indicator Variables. Multicollinearity. ANOVA II. Advanced Regression Diagnostic Methods. Influential Data Values. Measurement Errors and Instrumental Variables Regression. Weighted Least Squares and Robust Regression Procedures. Correlated Errors and Autoregressive Structures. Crossvalidation and Model Selection Methods. Advanced Regression Models. Biased Regression Methods and Regression Shrinkage. Piecewise and Nonparametric Methods. Regression Models with Censored Data. Nonlinear Regression. Regression Models with Counts as Responses. Multivariate Multiple Regression. Data Mining. Miscellaneous Topics. Appendices.
Notă biografică
Derek Young is an assistant professor of statistics at the University of Kentucky. He has over ten years of experience as a statistician, including positions in industry, government, and academia. During this time, he has also taught online courses in regression methods for Penn State University and the University of Kentucky. His research interests include (finite) mixture models, tolerance regions, and statistical computing.
Recenzii
"Covering a wide range of regression topics, this clearly written handbook explores not only the essentials of regression methods for practitioners but also a broader spectrum of regression topics for researchers. Complete and detailed, this unique, comprehensive resource provides an extensive breadth of topical coverage, some of which is not typically found in a standard text on this topic. Young (Univ. of Kentucky) covers such topics as regression models for censored data, count regression models, nonlinear regression models, and nonparametric regression models with autocorrelated data. In addition, assumptions and applications of linear models as well as diagnostic tools and remedial strategies to assess them are addressed. Numerous examples using over 75 real data sets are included, and visualizations using R are used extensively. Also included is a useful Shiny app learning tool; based on the R code and developed specifically for this handbook, it is available online. This thoroughly practical guide will be invaluable for graduate collections."~D. J. Gougeon, Choice Connect
"The list of calculated examples contains virtually every possible field of application of statistics, a small subset of them reads as follows: car sale data, cheese-tasting experiment data, credit loss data, hospital stays data, James Bond data, and wind direction data." ~Hans-Jurgen Schmidt (Potsdam), Zentralblatt MATH
"The list of calculated examples contains virtually every possible field of application of statistics, a small subset of them reads as follows: car sale data, cheese-tasting experiment data, credit loss data, hospital stays data, James Bond data, and wind direction data." ~Hans-Jurgen Schmidt (Potsdam), Zentralblatt MATH
Descriere
This book provides a broad survey of both standard and nonstandard regression. Topics include those typically covered in a Master's-level statistics regression course- simple/multiple linear regression, residual analysis, ANOVA and specialized topics like biased regression procedures, multivariate regression, and count regression models.