Ratio Estimators using Robust Regression

81 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Modified ratio estimators using robust regression methods (2019, methodology): “As a result, this article shows that estimators proposed based on the other robust methods used when there are outliers in data set are more efficient estimators than traditional estimators and Kadılar, Candan, and Çıngı (2007) estimators in both the real data and simulation study.”
An efficient family of robust-type estimators for the population variance in simple and stratified random sampling (2021, result): “When the dataset contains outliers, the classical estimators are negatively affected by this case and give fallacious results (Kadilar, Candan, and Cingi 2007).”
Estimation of the Mean of a Sensitive Variable in the Presence of Auxiliary Information (2012, background): “These include Ray and Singh (1981), Perri (2004), and Kadilar and Cingi (2004, 2006).”
Modified regression estimators using robust regression methods and covariance matrices in stratified random sampling (2020, methodology): “Zaman and Bulut (2018) suggested new ratio-type estimators to reduce the effect of the outlier problem by considering some of the robust methods in addition to Huber-M estimate used by Kadılar et al. (2007).”