Bootstrapping A Nonparametric Approach to Statistical Inference

Name: Bootstrapping A Nonparametric Approach to Statistical Inference
Availability: InStock
ISBN: 9780803953819

ISBN-10: 080395381X

ISBN-13: 9780803953819

Edition: 1993

Authors: Christopher Z. Mooney, Robert D. Duval

List price: $42.00

This item qualifies for FREE shipping.

30 day, 100% satisfaction guarantee!

Semester Rental: $19.09 Due date: 8/14/2024

Rental notice: supplementary materials (access codes, CDs, etc.) are not guaranteed with rental orders.

Buy used: $13.61

Buy new: $39.74

Marketplace

3 new & used from $7.28

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

Description:

"This book is. . . clear and well-written. . . anyone with any interest in the basis of quantitative analysis simply must read this book. . . . well-written, with a wealth of explanation. . ." --Dougal Hutchison in Educational Research Using real data examples, this volume shows how to apply bootstrapping when the underlying sampling distribution of a statistic cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, it discusses the advantages and limitations of four bootstrap confidence interval methods--normal approximation, percentile, bias-corrected percentile, and percentile-t. The book concludes with a convenient summary of how to…

Book details

List price: $42.00
Copyright year: 1993
Publisher: SAGE Publications, Incorporated
Publication date: 8/9/1993
Binding: Paperback
Pages: 80
Size: 5.30" wide x 8.30" long x 0.30" tall
Weight: 0.198
Language: English




Introduction


Traditional Parametric Statistical Inference


Bootstrap Statistical Inference


Bootstrapping a Regression Model


Theoretical Justification


The Jackknife


Monte Carlo Evaluation of the Bootstrap



Statistical Inference Using the Bootstrap


Bias Estimation


Bootstrap Confidence Intervals



Applications of Bootstrap Confidence Intervals


Confidence Intervals for Statistics With Unknown Sampling Distributions


Inference When Traditional Distributional Assumptions Are Violated



Conclusion


Future Work


Limitations of the Bootstrap


Concluding Remarks