
Accession Number : ADP007155
Title : QuasiRandom Resampling for the Bootstrap,
Corporate Author : AUSTRALIAN NATIONAL UNIV CANBERRA
Personal Author(s) : Do, KimAnh
Report Date : 1992
Pagination or Media Count : 4
Abstract : Quasirandom 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 ndimensional hypercube. From this idea we develop a quasirandom 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 quasirandom 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 quasirandom 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