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

Improved Mean Estimation of a Sensitive Variable Using Auxiliary Information in Stratified Sampling

2014 ◽  
Vol 17 (5-6) ◽  
pp. 503-518 ◽  
Author(s):  
Rita Sousa ◽  
Sat Gupta ◽  
Javid Shabbir ◽  
Pedro Corte-Real
Keyword(s):  
Auxiliary Information ◽  
Stratified Sampling ◽  
Mean Estimation ◽  
Sensitive Variable

Does Diversification Drive Down Risk-adjusted Returns? A Quantile Regression Approach

2017 ◽  
Vol 11 (2) ◽  
Author(s):  
Jeungbo Shim
Keyword(s):  
Quantile Regression ◽  
Insurance Industry ◽  
Estimation Method ◽  
Product Diversification ◽  
Return Distribution ◽  
Diversification Discount ◽  
Explanatory Variables ◽  
Heterogeneous Effects ◽  
Mean Estimation ◽  
Alternative Measures

AbstractThis study examines diversification-performance relationship in the U.S. property-liability insurance industry over the period of 1996–2010. Unlike prior studies that rely on the conditional mean estimation method, we employ quantile regression, which captures the heterogeneous effects of diversification on conditional return distribution. The results show that diversification does not necessarily drive down risk-adjusted returns and its effects vary along return distribution. We find that there is a diversification discount for firms in the lower levels of return distribution, whereas a diversification premium exists for firms in the upper levels of return distribution. We provide evidence that the relations between risk-adjusted returns and other explanatory variables are not constant, but vary over the quantiles of return distribution. Our results are robust to alternative measures of an insurer’s performance and product diversification.

scholarly journals Finite Population Mean Estimation through a Two-Parameter Ratio Estimator Using Auxiliary Information in Presence of Non-Response

2016 ◽  
Vol 12 (2) ◽  
pp. 5-39 ◽  
Author(s):  
S. K. Pal ◽  
H. P. Singh
Keyword(s):  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Human Populations ◽  
Ratio Estimator ◽  
Population Mean ◽  
Parameter Ratio ◽  
Mean Estimation ◽  
Large Sample Approximation ◽  
Respondent Group ◽  
Sample Approximation

Abstract In surveys covering human populations it is observed that information in most cases are not obtained at the first attempt even after some callbacks. Such problems come under the category of non-response. Surveys suffer with non-response in various ways. It depends on the nature of required information, either surveys is concerned with general or sensitive issues of a society. Hansen and Hurwitz (1946) have considered the problem of non-response while estimating the population mean by taking a subsample from the non-respondent group with the help of extra efforts and an estimator was suggested by combining the information available from the response and nonresponse groups. We also mention that in survey sampling auxiliary information is commonly used to improve the performance of an estimator of a quantity of interest. For estimating the population mean using auxiliary information in presence of non-response has been discussed by various authors. In this paper, we have developed estimators for estimating the population mean of the variable under interest when there is non-response error in the study as well as in the auxiliary variable. We have studied properties of the suggested estimators under large sample approximation. Comparison of the suggested estimators with usual unbiased estimator reported by Hansen and Hurwitz (1946) and the ratio estimator due to Rao (1986) have been made. The results obtained are illustrated with aid of an empirical study.

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.

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