Introduction to bootstrap methods with applications to R
Material type: TextPublication details: Hoboken, N.J. : Wiley, c2011Description: xvii, 216 p. : illISBN: 9780470467046 Subject(s): Bootstrap-Statistics | R-Computer program language | StatisticsDDC classification: 519.54 Summary: "This book provides both an elementary and a modern introduction to the bootstrap for students who do not have an extensive background in advanced mathematics. It offers reliable, hands-on coverage of the bootstrap's considerable advantages -- as well as its drawbacks. The book outpaces the competition by skillfully presenting results on improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems. To alert readers to the limitations of the method, the book exhibits counterexamples to the consistency of bootstrap methods. The authors take great care to draw connections between the more traditional resampling methods and the bootstrap, oftentimes displaying helpful computer routines in R. Emphasis throughout the book is on the use of the bootstrap as an exploratory tool including its value in variable selection and other modeling environments"--Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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BK | Kannur University Central Library Stack | Stack | 519.54 CHE/I (Browse shelf (Opens below)) | Available | 37228 |
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519.536 SCH/U Understanding regression analysis : | 519.5360285513 DEB/L Linear models and regression with R : an integrated approach | 519.538 FAR/L Linear models with R | 519.54 CHE/I Introduction to bootstrap methods with applications to R | 519.54 RAD/L Linear Statistical Inference and its applications / | 519.54 RAD/L Linear Statistical Inference and its applications / | 519.54 RAD/L Linear Statistical Inference and its applications / |
"This book provides both an elementary and a modern introduction to the bootstrap for students who do not have an extensive background in advanced mathematics. It offers reliable, hands-on coverage of the bootstrap's considerable advantages -- as well as its drawbacks. The book outpaces the competition by skillfully presenting results on improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems. To alert readers to the limitations of the method, the book exhibits counterexamples to the consistency of bootstrap methods. The authors take great care to draw connections between the more traditional resampling methods and the bootstrap, oftentimes displaying helpful computer routines in R. Emphasis throughout the book is on the use of the bootstrap as an exploratory tool including its value in variable selection and other modeling environments"--
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