Accession Number:

ADP007155

Title:

Quasi-Random Resampling for the Bootstrap,

Descriptive Note:

Corporate Author:

AUSTRALIAN NATIONAL UNIV CANBERRA

Personal Author(s):

• Do, Kim-Anh

Report Date:

1992-01-01

Pagination or Media Count:

4.0

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