A Robust Regression Type Estimator for Estimating Population Mean under Non-Normality in the Presence of Non-Response
Keywords:
regression type estimator, modified maximum likelihood, robust linear regression, super-population, simulation study, non-response
Abstract
In sampling theory, regression type estimators are extensively used to estimate the population mean when the correlation between study and auxiliary variables is high. In this study, we incorporate robust modified maximum likelihood estimators (MMLEs) into regression type estimator in the presence of non-response and their properties have been obtained theoretically. For the support of the theoretical outcomes, simulations under several super-population models have been made. We study the robustness properties of these modified estimators. We show that utilization of MMLEs in estimating finite populations mean leads to robust estimates, which is very advantageous when we have non-normality or other common data anomalies such as outliers.
Downloads
- Article PDF
- TEI XML Kaleidoscope (download in zip)* (Beta by AI)
- Lens* NISO JATS XML (Beta by AI)
- HTML Kaleidoscope* (Beta by AI)
- DBK XML Kaleidoscope (download in zip)* (Beta by AI)
- LaTeX pdf Kaleidoscope* (Beta by AI)
- EPUB Kaleidoscope* (Beta by AI)
- MD Kaleidoscope* (Beta by AI)
- FO Kaleidoscope* (Beta by AI)
- BIB Kaleidoscope* (Beta by AI)
- LaTeX Kaleidoscope* (Beta by AI)
How to Cite
Published
2015-05-15
Issue
Section
License
Copyright (c) 2015 Authors and Global Journals Private Limited
This work is licensed under a Creative Commons Attribution 4.0 International License.