UNBIASED ESTIMATION WHEN SAMPLING FROM RENEWAL PROCESSES - THE SINGLE SAMPLE AND K-SAMPLE RANDOM MEANS CASES
成果类型：
Article
署名作者：
KREMERS, WK; ROBSON, DS
署名单位：
Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Cornell University
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
发表日期：
1987
页码：
329336
关键词：
摘要：
Suppose random variables are sampled sequentially until the sum of associated nonnegative random variables is greater than or equal to a predetermined constant. When no distributional asumptions are made, we derive uniform minimum variance unbiased estimators for functions which are U-estimable in the fixed sample size case. We also consider estimation for some special cases when distributional assumptions are made. When estimating the mean and variance of random means, it is often assumed that sample sizes are independent of populations selected for sampling, and hence independent of the random means associated with the populations sampled. When sampling from renewal processes sample size is dependent on the distribution of the population selected for sampling. Thus, in addition to the single sample case we consider the k-sample problem and the estimation of the expectation and variance of random means.