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The stochastic precedence ordering with applications in sampling and testing.
Boland-PJ; Singh-H; Cukic-B
J Appl Probab 2004 Mar; 41(1):73-82
Stratified and simple random sampling (or testing) are two common methods used to investigate the number or proportion of items in a population with a particular attribute. Although it is known that cost factors and information about the strata in the population are often crucial in deciding whether to use stratified or simple random sampling in a given situation, the stochastic precedence ordering for random variables can also provide the basis for an interesting criteria under which these methods may be compared. It may be particularly relevant when we are trying to find as many special items as possible in a population (for example individuals with a disease in a country). Properties of this total stochastic order on the class of random variables are discussed, and necessary and sufficient conditions are established which allow the comparison of the number of items of interest found in stratified random sampling with the number found in simple random sampling in the stochastic precedence order. These conditions are compared with other results established on stratified and simple random sampling (testing) using different stochastic-order-type criteria, and applications are given for the comparison of sums of Bernoulli random variables and binomial distributions.
Sampling; Statistical-analysis; Mathematical-models; Author Keywords: Stratified random sampling; simple random sampling; partition and subdomain testing; software faults; testing domain; subdomain; Bernoulli distribution; binomial distribution; stochastic order; stochastic precedence order; mean order; arithmetic mean; geometric mean; complimentary geometric mean
Department of Statistics, West Virginia University, PO Box 6330, Morgantown, WV 26506, USA
Issue of Publication
Journal of Applied Probability
Page last reviewed: August 23, 2019
Content source: National Institute for Occupational Safety and Health Education and Information Division