期刊
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
卷 80, 期 1-2, 页码 143-157出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00949650802549135
关键词
genetic algorithm; adaptive design; staggered design; Weibull; censoring
Shelf life is a specified percentile of the time-until-spoilage distribution of a food product. This paper investigates statistical properties of various estimators of shelf life and develops a genetic algorithm for finding near-optimal staggered designs for estimation of shelf life. MLEs and their associated confidence intervals for shelf life have smaller bias, better performance, and better coverage than the corresponding ad hoc regression-based estimates. However, performance of MLEs for common sample sizes must be evaluated by simulation. The genetic algorithm, coded as an SAS macro, searched the design space well and generated near-optimal designs as measured by improvement to a simulation-based performance measure.
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