Linear contrasts in one-way classification AR(1) model with gamma innovations

被引：0

作者：

Senoglu, Birdal ^{[1
]}

Bayrak, Ozlem Turker ^{[2
]}

机构：

[1] Ankara Univ, Dept Stat, Fac Sci, Do Gol Caddesi, TR-06100 Ankara, Turkey

[2] Cankaya Univ, Stat Unit, Ankara, Turkey

来源：

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS | 2016年 / 45卷 / 06期

关键词：

Autoregressive model; linear contrasts; nonnormality; robustness; modified likelihood; gamma distribution; TIME-SERIES MODELS; AUTOREGRESSIVE MODELS; ESTIMATING PARAMETERS; NONNORMAL SITUATIONS; MAXIMUM-LIKELIHOOD; DESIGN;

D O I：

10.15672/HJMS.20164515996

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this study, the explicit estimators of the model parameters in oneway classification AR(1) model with gamma innovations are derived by using modified maximum likelihood (MML) methodology. We also propose a new test statistic for testing linear contrasts. Monte Carlo simulation results show that the MML estimators have higher efficiencies than the traditional least squares (LS) estimators and the proposed test has much better power and robustness properties than the normal theory test.

引用

页码：1743 / 1754

页数：12

共 17 条

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