Inference Based on Generalized Doubly Type-II Hybrid Censored Sample from a Half Logistic Distribution

Inference Based on Generalized Doubly Type-II Hybrid Censored Sample from a Half Logistic Distribution

Chandrasekar et al. (2004) introduced a generalized Type-II hybrid censoring. In this paper, we propose generalized doubly Type-II hybrid censoring. In addition, this paper presents the statistical inference on the scale parameter \sigma for the half logistic distribution when samples are generalized doubly Type-II hybrid censoring. The approximate maximum likelihood(AMLE) method is developed to estimate the unknown parameter. The scale parameter sigma is estimated by the AMLE method using two different Taylor series expansion types. We compare the AMLEs in the sense of the mean square error(MSE). The simulation procedure is repeated 10,000 times for the sample size n=20, 30, 40 and various censored samples. The AMLE_I is better than AMLE_(II) in the sense of the MSE.

Chandrasekar et al. (2004) introduced a generalized Type-II hybrid censoring. In this paper, we propose generalized doubly Type-II hybrid censoring. In addition, this paper presents the statistical inference on the scale parameter \sigma for the half logistic distribution when samples are generalized doubly Type-II hybrid censoring. The approximate maximum likelihood(AMLE) method is developed to estimate the unknown parameter. The scale parameter sigma is estimated by the AMLE method using two different Taylor series expansion types. We compare the AMLEs in the sense of the mean square error(MSE). The simulation procedure is repeated 10,000 times for the sample size n=20, 30, 40 and various censored samples. The AMLE_I is better than AMLE_(II) in the sense of the MSE.