Modified ratio estimators using robust regression methods

Robust regression tools are commonly used to develop regression-type ratio estimators with traditional measures of location whenever data are contaminated with outliers. Recently, the researchers extended this idea and developed regression-type ratio estimators through robust minimum covariance determinant (MCD) estimation. In this study, the quantile regression with MCD-based measures of location is utilized and a class of quantile regression-type mean estimators is proposed. The mean squared errors (MSEs) of the proposed estimators are also obtained. The proposed estimators are compared with the reviewed class of estimators through a simulation study. We also incorporated two real-life applications. To assess the presence of outliers in these real-life applications, the Dixon chi-squared test is used. It is found that the quantile regression estimators are performing better as compared to some existing estimators.

Section: Review Of Mcd-based Ratio Estimatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Minimum Covariance Determinant-Based Quantile Robust Regression-Type Estimators for Mean Parameter

Shahzad

Al‐Noor

Afshan

et al. 2021

“…Noor-ul-Amin et al [2] proposed to use Huber estimate, instead of LSE under double sampling. In ratio estimators, Zaman and Bulut [3] improved the estimators by applying robust regression coefficients. Zaman [4] provided estimators combining ratio estimators for Y utilizing the ratio estimators given in Zaman and Bulut [3].…”

Section: Introductionmentioning

confidence: 99%

Robust Regression-Ratio-Type Estimators of the Mean Utilizing Two Auxiliary Variables: A Simulation Study

Zaman

Dünder

Audu

et al. 2021

Self Cite

Many authors defined the modified version of the mean estimator by using two auxiliary variables. These proposed estimators highly depend on the calculated regression coefficients. In the presence of outliers, these estimators do not give satisfactory results. In this study, we improve the suggested estimators using several robust regression techniques while obtaining the regression coefficients. We compared the efficiencies between the suggested estimators and the estimators presented in the literature. We used two numerical examples and a simulation study to support these theoretical results. Empirical results show that the modified ratio estimator performs well in the presence of outliers when adopting robust regression techniques.

“…Overall, auxiliary information can be of the accompanying two sorts such as auxiliary information of definite nature such as census information, assumptions of experts, or else linear limitations, and another sort is information known with some level of uncertainty, for example, survey results and estimations of previous analysis. For detailed study, interested readers may refer to [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Mean Estimators Using Robust Quantile Regression and L-Moments’ Characteristics for Complete and Partial Auxiliary Information

Anas

Huang

Alilah

et al. 2021

Ratio type regression estimator is a prevalent and readily implemented heuristic under simple random sampling (SRS) and two-stage sampling for the estimation of population. But this existing method is based on the ordinary least square (OLS) regression coefficient which is not an effective approach in the presence outliers in the data. In this article, we proposed a class of estimators firstly for complete auxiliary information and, later on, for partial auxiliary information for the presence of outliers in the data. To address this problem, initially we presented a distinct class of estimators by introducing the characteristics of L-moments in the existing estimators. Later on, quantile regression estimators are defined as more robust in the presence of outliers. These techniques empowered the proposed estimators to handle the problem of outliers. To prove the better performance of the proposed estimators, numerical studies are carried out using R language. To calculate the mean square error (MSE), hypothetical equations are expressed for adapted and proposed estimators. Percentage Relative Efficiencies (PRE) are compared to justify the proposed estimators.