2018/07/02 by Deepak Prajapati, Prajapati, Deepak, Sharmistha Mitra +3 · 1 citation
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Distribution Estimation and Applications #Survey Sampling and Estimation Techniques
paper · pdf · doi:10.48550/arxiv.1807.00615
openalex publication_date 2018/07/02 · openalex created_date 2022/08/04 · openalex updated_date 2026/07/28
The study proposes a new decision theoretic sampling plan (DSP) for Type-I\nand Type-I hybrid censored samples when the lifetimes of individual items are\nexponentially distributed with a scale parameter. The DSP is based on an\nestimator of the scale parameter which always exists, unlike the MLE which may\nnot always exist. Using a quadratic loss function and a decision function based\non the proposed estimator, a DSP is derived. To obtain the optimum DSP, a\nfinite algorithm is used. Numerical results demonstrate that in terms of the\nBayes risk, the optimum DSP is as good as the Bayesian sampling plan (BSP)\nproposed by citelin2002bayesian and citeliang2013optimal. The proposed\nDSP performs better than the sampling plan of citeLam1994bayesian and\n citelin2008-10exact in terms of Bayes risks. The main advantage of the\nproposed DSP is that for higher degree polynomial and non-polynomial loss\nfunctions, it can be easily obtained as compared to the BSP.\n