Author(s):
1. Aamir Raza:
Department of Statistics, Government College Women University Sialkot, Pakistan.
2. Muhammad Noor-ul-Amin:
Department of Statistics, COMSATS University, Islamabad
Lahore Campus, Pakistan.
Abstract:
The ordinary least square (OLS) produced inefficient estimates of population mean when data have outliers. The redescending M-estimators are used as alternate method to tackle the effect of outliers. In this article, we have proposed an efficient class of regression-in-ratio estimators to estimate the population mean in the presence of outliers using robust regression in ranked set sampling schemes. Performance of proposed class of regression-in-ratio estimators is compared with existing estimators in considered sampling schemes using mean square error. The proposed class of estimators is found to be more efficient in all considered ranked set sampling schemes. A real-life data example and extensive simulation study are included to validate the results.
Page(s):
329-347
DOI:
DOI not available
Published:
Journal: Pakistan Journal of Statistics, Volume: 39, Issue: 3, Year: 2023
Keywords:
Ranked set sampling
,
Outliers
,
Redescending MEstimator
,
Robust Regression
References:
References are not available for this document.
Citations
Citations are not available for this document.