Accommodation of outliers by robust MML estimation for spatial autoregressive model

被引：0
|
作者：
Sweta Shukla
S. Lalitha
Pulkit Srivastava
机构：
[1] Institute of Applied Sciences and Humanities,Department of Mathematics
[2] GLA University,Department of Statistics
[3] University of Allahabad,Department of Statistics
[4] Faculty of Mathematical Sciences,undefined
[5] University of Delhi,undefined
来源：
International Journal of System Assurance Engineering and Management | 2023年 / 14卷
关键词：
MML estimation; Spatial autoregressive model ; Outlier; Robust; Type II censoring;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Some outliers might be undetected even after using the outlier detection procedures. In this paper, an accommodation procedure for such undetected outliers is discussed, which is done by robust modified maximum likelihood (MML) estimation of Type II censored sample for a Spatial Autoregressive (SAR) model. A new method is proposed for determining the number of observation to be censored to obtain robust parameter estimates. Next, a Monte-Carlo simulation study is carried to assess the robustness of the obtained MML estimators for both the normal and the contaminated normal models. Also, asymptotic variances, covariances, and distributional results are obtained for the MML estimators.
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页码：293 / 306
页数：13
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