Explores the application of bootstrap to problems that place unusual demands on the method. The bootstrap method, introduced by Bradley Efron in 1973, is a nonparametric technique for inferring the distribution of a statistic derived from a sample. Most of the papers were presented at a special meeting sponsored by the Institute of Mathematical Statistics and the Interface Foundation in May, 1990.
Partial table of contents:
GENERAL PRINCIPLES OF THE BOOTSTRAP.
On the Bootstrap of M-Estimators and Other Statistical Functionals (M. Arcones & E. Gine).
Bootstrapping Markov Chains (K. Athreya & C. Fuh).
Six Questions Raised by the Bootstrap (B. Efron).
Efficient Bootstrap Simulation (P. Hall).
Bootstrapping Signs (R. LePage).
Bootstrap Bandwidth Selection (J. Marron).
APPLICATIONS OF THE BOOTSTRAP.
A Generalized Bootstrap (E. Bedrick & J. Hill).
Bootstrapping Admissible Linear Model Selection Procedures (D. Brownstone).
A Hazard Process for Survival Analysis (J. Hsieh).
A Nonparametric Density Estimation Based Resampling Algorithm (M. Taylor & J. Thompson).
Nonparametric Rank Estimation Using Bootstrap Resampling and Canonical Correlation Analysis (X. Tu, et al.).
Series: Wiley Series in Probability and Statistics
Number Of Pages: 448
Published: 24th February 1992
Country of Publication: US
Dimensions (cm): 24.0 x 16.5
Weight (kg): 0.84
Edition Number: 1