One-step M-estimators: Jones and Faddy's skewed t-distribution

被引:20
作者
Acitas, Sukru [1 ]
Kasap, Pelin [2 ]
Senoglu, Birdal [3 ]
Arslan, Olcay [3 ]
机构
[1] Anadolu Univ, Dept Stat, TR-26470 Eskisehir, Turkey
[2] Ondokuz Mayis Univ, Dept Stat, TR-55139 Samsun, Turkey
[3] Ankara Univ, Dept Stat, TR-06100 Ankara, Turkey
关键词
one-step M-estimator; modified likelihood; regression; robustness; efficiency; PARTIALLY ADAPTIVE ESTIMATION; MAXIMUM-LIKELIHOOD; ROBUST ESTIMATION; REGRESSION; ASYMPTOTICS; PARAMETERS; LOCATION;
D O I
10.1080/02664763.2013.788620
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
070103 [概率论与数理统计]; 140311 [社会设计与社会创新];
摘要
One-step M (OSM)-estimator needs some initial/preliminary estimates at the beginning of the calculation process. In this study, we propose to use new initial estimates for the calculation of the OSM-estimator. We consider simple location and simple linear regression models when the distribution of the error terms is Jones and Faddy's skewed t. Monte-Carlo simulation study shows that the OSM estimator(s) based on the proposed initial estimates is/are more efficient than the OSM estimator(s) based on the traditional initial estimates especially for the skewed cases. We also analyze some real data sets taken from the literature at the end of the paper.
引用
收藏
页码:1545 / 1560
页数:16
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