scholarly journals Efficient Quantile Regression with Auxiliary Information

2013 ◽  
pp. 365-374
Author(s):  
Ursula U. Müller ◽  
Ingrid Van Keilegom
Keyword(s):  
Quantile Regression ◽  
Auxiliary Information

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

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Malik Muhammad Anas ◽  
Zhensheng Huang ◽  
David Anekeya Alilah ◽  
Ambreen Shafqat ◽  
Sajjad Hussain
Keyword(s):  
Quantile Regression ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Least Square ◽  
R Language ◽  
Ordinary Least Square ◽  
Numerical Studies ◽  
Regression Estimators ◽  
L Moments ◽  
Class Of Estimators

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.

scholarly journals Quantile regression-ratio-type estimators for mean estimation under complete and partial auxiliary information

2020 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Usman Shahzad ◽  
Muhammad Hanif ◽  
Irsa Sajjad ◽  
Malik Anas
Keyword(s):  
Quantile Regression ◽  
Auxiliary Information ◽  
Mean Estimation

Quantile Regression

2005 ◽  
Author(s):  
Roger Koenker
Keyword(s):  
Quantile Regression

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

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