Accession Number : ADP007155

Title :   Quasi-Random Resampling for the Bootstrap,

Corporate Author : AUSTRALIAN NATIONAL UNIV CANBERRA

Personal Author(s) : Do, Kim-Anh

Report Date : 1992

Pagination or Media Count : 4

Abstract : Quasi-random sequences are known to give efficient numerical integration rules in many Bayesian statistical problems where the posterior distribution can be transformed into periodic functions on the n-dimensional hypercube. From this idea we develop a quasi-random approach to the generation of resamples used for Monte Carlo approximations to bootstrap estimates of bias, variance and distribution functions. We demonstrate a major difference between quasi-random bootstrap resamples, which are generated by deterministic algorithms and have no true randomness, and the usual pseudorandom bootstrap resamples generated by the classical bootstrap approach. Various quasi-random approaches are considered and are shown via a simulation study to result in approximants that are competitive in terms of efficiency when compared with other bootstrap Monte Carlo procedures such as balanced and antithetic resampling.

Descriptors :   *DISTRIBUTION FUNCTIONS, *NUMERICAL INTEGRATION, *PERIODIC FUNCTIONS, ALGORITHMS, APPROACH, BIAS, DISTRIBUTION, EFFICIENCY, ESTIMATES, FUNCTIONS, INTEGRATION, SEQUENCES, SIMULATION.

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE