Estimation for the three-parameter gamma distribution based on progressively censored data

作     者：Basak, Indrani Balakrishnan, N. 

作者机构：Penn State Altoona Altoona PA USA McMaster Univ Hamilton Hamilton ON Canada 

出 版 物：《STATISTICAL METHODOLOGY》 (统计方法论)

年 卷 期：2012年第9卷第3期

页      面：305-319页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

主　　题：Progressive censoring Missing value principle Maximum likelihood estimators Iterative procedure Moment estimators Coverage probabilities Monte Carlo simulation 

摘      要：Some work has been done in the past on the estimation for the three-parameter gamma distribution based on complete and censored samples. In this paper, we develop estimation methods based on progressively Type-II censored samples from a three-parameter gamma distribution. In particular, we develop some iterative methods for the determination of the maximum likelihood estimates (MLEs) of all three parameters. It is shown that the proposed iterative scheme converges to the MLEs. In this context, we propose another method of estimation which is based on missing information principle and moment estimators. Simple alternatives to the above two methods are also suggested. The proposed estimation methods are then illustrated with a numerical example. We also consider the interval estimation based on large-sample theory and examine the actual coverage probabilities of these confidence intervals in case of small samples using a Monte Carlo simulation study. (C) 2011 Elsevier B.V. All rights reserved.