Aslam Muhammad, Rao Gadde Srinivasa, Albassam Mohammed
Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia.
Department of Mathematics and Statistics, University of Dodoma, Dodoma P.O. Box 259, Tanzania.
Int J Environ Res Public Health. 2022 Apr 27;19(9):5308. doi: 10.3390/ijerph19095308.
The purpose of this paper is to develop a multiple dependent state (MDS) sampling plan based on time-truncated sampling schemes for the daily number of cases of the coronavirus disease COVID-19 using gamma distribution under indeterminacy. The proposed sampling scheme parameters include average sample number (ASN) and accept and reject sample numbers when the indeterminacy parameter is known. In addition to the parameters of the proposed sampling schemes, the resultant tables are provided for different known indeterminacy parametric values. The outcomes resulting from various sampling schemes show that the ASN decreases as indeterminacy values increase. This shows that the indeterminacy parameter plays a vital role for the ASN. A comparative study between the proposed sampling schemes and existing sampling schemes based on indeterminacy is also discussed. The projected sampling scheme is illustrated with the help of the daily number of cases of COVID-19 data. From the results and real example, we conclude that the proposed MDS sampling scheme under indeterminacy requires a smaller sample size compared to the single sampling plan (SSP) and the existing MDS sampling plan.
本文的目的是在不确定性情况下,基于时间截尾抽样方案,利用伽马分布为新型冠状病毒肺炎(COVID-19)每日病例数制定一种多重相关状态(MDS)抽样方案。所提出的抽样方案参数包括平均样本量(ASN)以及在不确定性参数已知时的接受和拒绝样本数。除了所提出抽样方案的参数外,还针对不同的已知不确定性参数值提供了相应表格。各种抽样方案的结果表明,随着不确定性值的增加,ASN会减少。这表明不确定性参数对ASN起着至关重要的作用。还讨论了所提出的抽样方案与基于不确定性的现有抽样方案之间的比较研究。借助COVID-19每日病例数数据对预计的抽样方案进行了说明。从结果和实际例子来看,我们得出结论,在不确定性情况下所提出的MDS抽样方案与单抽样方案(SSP)和现有的MDS抽样方案相比,所需样本量更小。