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Robustness of Best Linear Unbiased Estimators based on Order Statistics

Abstract Number:

3615 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Nubaira Rizvi (1), Masuma Mannan (1), Evrim Oral (1)

Institutions:

(1) LSUHSC School of Public Health Biostatistics and Data Science Program, New Orleans, LA

Co-Author(s):

Masuma Mannan  
LSUHSC School of Public Health Biostatistics and Data Science Program
Evrim Oral  
LSUHSC School of Public Health Biostatistics and Data Science Program

First Author:

Nubaira Rizvi  
LSUHSC School of Public Health Biostatistics and Data Science Program

Presenting Author:

Nubaira Rizvi  
N/A

Abstract Text:

Recently, Sanaullah et al (2019) and Ahmad et al (2023) utilized a Best Linear Unbiased Estimator (BLUE) that is based on order statistics to propose novel ratio-type estimators in survey sampling. While they studied the properties of the proposed ratio estimators in the survey sampling setting, the robustness properties of the BLUE-type estimators they used have not been thoroughly investigated. Therefore, in this study, we evaluate the robustness properties of the BLUE-type location estimators and compare them to other well-known robust estimators such as Huber's M and Tiku's modified maximum likelihood estimator using an extensive simulation study. Additionally, we demonstrate the performance of the estimators through a real-life example.

Keywords:

BLUE|Modified Maximum Likelihood|Order Statistics|Location-scale families
|Robustness
|

Sponsors:

IMS

Tracks:

Statistical Methodology

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