Introduction Bootstrap Methods Applications by Chernick Michael (14 results)

Hardcover. Condition: Fine. No Jacket. 1st Edition. Fine copy in pictorial hardcover. Math.

Condition: new.

Condition: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,550grams, ISBN:9780470467046. …

Condition: New. In English.

Hardback. Condition: New. New copy - Usually dispatched within 4 working days.

Condition: New. pp. xvii + 216 Illus.

Condition: New. A comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. Num Pages: 240 pages, Illustrations. BIC Classification: PBT; TJ. Category: (P) Professional & Vocational. Dimension: 245 x 158 x 60. Weight in Grams: 452. . 2011. 1st Edition. Hardcover. . . . .…

Condition: New. pp. xvii + 216 Index.

Condition: New. A comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. Num Pages: 240 pages, Illustrations. BIC Classification: PBT; TJ. Category: (P) Professional & Vocational. Dimension: 245 x 158 x 60. Weight in Grams: 452. . 2011. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.…

Hardcover. Condition: Brand New. 1st edition. 240 pages. 9.30x6.20x0.70 inches. In Stock.

Hardcover. Condition: gut. An Introduction to Bootstrap Methods with Applications to R In englischer Sprache. pages.

Hardcover. Condition: Brand New. 1st edition. 240 pages. 9.30x6.20x0.70 inches. In Stock. This item is printed on demand.

Hardcover. Condition: new. Hardcover. A comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully utilizes R to illustrate applications for the bootstrap and other resampling methods. This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics. Emphasis throughout is on the use of bootstrap methods as an exploratory tool, including its value in variable selection and other modeling environments. The authors begin with a description of bootstrap methods and its relationship to other resampling methods, along with an overview of the wide variety of applications of the approach. Subsequent chapters offer coverage of improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems, including pharmaceutical, genomics, and economics. To inform readers on the limitations of the method, the book also exhibits counterexamples to the consistency of bootstrap methods. An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented throughout the book. A related website houses the book's R subroutines, and an extensive listing of references provides resources for further study. Discussing the topic at a remarkably practical and accessible level, An Introduction to Bootstrap Methods with Applications to R is an excellent book for introductory courses on bootstrap and resampling methods at the upper-undergraduate and graduate levels. It also serves as an insightful reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding of bootstrap methods. A comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condition: New. PRINT ON DEMAND pp. xvii + 216 Acknowledgements.