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A comparison of preliminary test, Stein-type and penalty estimators in gamma regression model
被引:6
|作者:
Mahmoudi, Akram
[1
]
Belaghi, Reza Arabi
[2
]
Mandal, Saumen
[3
]
机构:
[1] Jonkoping Univ, Jonkoping Int Business Sch, Jonkoping, Sweden
[2] Univ Tabriz, Fac Math Sci, Dept Stat, Tabriz, Iran
[3] Univ Manitoba, Dept Stat, Winnipeg, MB, Canada
基金:
加拿大自然科学与工程研究理事会;
关键词:
Gamma regression;
preliminary test estimator;
shrinkage estimator;
penalty estimator;
asymptotic properties;
Monte Carlo simulation;
prostate cancer;
SHRINKAGE;
PRETEST;
D O I:
10.1080/00949655.2020.1795174
中图分类号:
TP39 [计算机的应用];
学科分类号:
081203 ;
0835 ;
摘要:
Owing to the broad applicability of gamma regression, we propose some improved estimators based on the preliminary test and Stein-type strategies to estimate the unknown parameters in a gamma regression model. These estimators are considered when it is suspected that the parameters may be restricted to a subspace of the parameter space. Two penalty estimators such as LASSO and ridge regression are also presented. An asymptotic theory for the preliminary test and Stein-type estimators is developed, and asymptotic distributional bias and asymptotic quadratic risk of the proposed estimators are obtained. Comprehensive Monte-Carlo simulation experiments are conducted. Comparisons are then made based on simulated relative efficiency to clarify the performance of the proposed estimators. Practitioners are recommended to use the positive-part Stein-type estimator since its performance is robust irrespective of the reliability of the subspace information. A real data on prostate cancer is considered to illustrate the performance of the proposed estimators.
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页码:3051 / 3079
页数:29
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