By: R Lepage
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.). Index.
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