Parameter and quantile estimation for the three-parameter gamma distribution based on statistics invariant to unknown location

被引：7

作者：

Nagatsuka, Hideki ^{[1
]}

Balakrishnan, N. ^{[2
]}

机构：

[1] Tokyo Metropolitan Univ, Fac Syst Design, Hino, Tokyo 1910065, Japan

[2] McMaster Univ, Dept Math & Stat, Hamilton, ON L8S 4K1, Canada

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2012年 / 142卷 / 07期

关键词：

Maximum likelihood estimators; Modified moment estimators; Bayesian likelihood estimators; Order statistics; Threshold parameter; Consistency; MAXIMUM-LIKELIHOOD-ESTIMATION; MODIFIED MOMENT;

D O I：

10.1016/j.jspi.2012.01.018

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The three-parameter gamma distribution is widely used as a model for distributions of life spans, reaction times, and for other types of skewed data. In this paper, we propose an efficient method of estimation for the parameters and quantiles of the three-parameter gamma distribution, which avoids the problem of unbounded likelihood, based on statistics invariant to unknown location. Through a Monte Carlo simulation study, we then show that the proposed method performs well compared to other prominent methods in terms of bias and mean squared error. Finally, we present two illustrative examples. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：2087 / 2102

页数：16

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