Resumen de Progressive censoring under inverse sampling for nonparametric multi-sample location problem
Gopaldeb Chattopadhyay, Indranil Mukhopadhyay
In this study, we propose a class of distribution-free progressive censoring test procedures for multi-sample location problem using a stage-wise partially sequential sampling technique. For this, we draw a fixed number of sample observations from one of the populations (say, control) and random number of observations from other populations (say, treatments) using suitable stopping rules. At each stage, two types of control groups, termed as fixed control group (FCG) and updated control group (UCG), are considered. Suitable stopping rules are constructed based on quantiles of the FCG and UCG observations separately. At each stage, FCG consists of initial control observations only; while UCG consists of stage-wise updated combined control observations and previous treatment observations. We examined different large sample results of the proposed tests. We also performed numerical studies to compare the performance of the tests based on FCG and UCG procedures. In addition, we compared the performance of the progressive censoring test procedures with the corresponding competitive terminal test procedure numerically.
