| Popis: |
In this paper, we design a decision theoretic sampling plan (DSP) based on Type-I and Type-I hybrid censored lifetime data from a one-parameter exponential distribution. The Bayes estimator of the mean lifetime is used to define a decision function. A suitable loss function is considered to derive the Bayes risk of this DSP. A finite algorithm is provided to obtain the optimum DSP and the corresponding Bayes risk. It has been observed numerically that the optimum DSP is better than the sampling plan proposed by Lam (Ann Stat 22:696–711, 1994) and Lin et al. (Commun Stat Simul Comput 37:1101–1116, 2008; Commun Stat Simul Comput 39:1499–1505, 2010) and it is as good as the Bayesian sampling plan (BSP) of Lin et al. (Ann Inst Stat Math 54:100–113, 2002) and Liang and Yang (J Stat Comput Simul 83: 922–940, 2013). It is observed that the Bayes risk of the optimum DSP is approximately equal to the Bayes risk of the BSP. In case of higher degree polynomials and for a non-polynomial loss function the DSP can be obtained without any additional effort as compared to BSP. |
