A New Class of L-Moments Based Calibration Variance Estimators  

作　　者：Usman Shahzad Ishfaq Ahmad Ibrahim Mufrah Almanjahie Nadia H.Al Noor Muhammad Hanif 

机构地区：[1]Department of Mathematics and Statistics,International Islamic University,Islamabad,46000,Pakistan [2]Department of Mathematics and Statistics,PMAS-Arid Agriculture University,Rawalpindi,46300,Pakistan [3]Department of Mathematics,King Khalid University,Abha,62529,Saudi Arabia [4]Statistical Research and Studies Support Unit,King Khalid University,Abha,62529,Saudi Arabia [5]Department of Mathematics,College of Science,Mustansiriyah University,Baghdad,10011,Iraq

出　　处：《Computers, Materials & Continua》2021年第3期3013-3028,共16页计算机、材料和连续体（英文）

基　　金：The authors are grateful to the Deanship of Scientific Research at King Khalid University,Kingdom of Saudi Arabia for funding this study through the research groups program under project number R.G.P.2/67/41.Ibrahim Mufrah Almanjahie received the grant.

摘　　要：Variance is one of themost important measures of descriptive statistics and commonly used for statistical analysis.The traditional second-order central moment based variance estimation is a widely utilized methodology.However,traditional variance estimator is highly affected in the presence of extreme values.So this paper initially,proposes two classes of calibration estimators based on an adaptation of the estimators recently proposed by Koyuncu and then presents a new class of L-Moments based calibration variance estimators utilizing L-Moments characteristics(L-location,Lscale,L-CV)and auxiliary information.It is demonstrated that the proposed L-Moments based calibration variance estimators are more efficient than adapted ones.Artificial data is considered for assessing the performance of the proposed estimators.We also demonstrated an application related to apple fruit for purposes of the article.Using artificial and real data sets,percentage relative efficiency(PRE)of the proposed class of estimators with respect to adapted ones are calculated.The PRE results indicate to the superiority of the proposed class over adapted ones in the presence of extreme values.In this manner,the proposed class of estimators could be applied over an expansive range of survey sampling whenever auxiliary information is available in the presence of extreme values.

关 键 词：L-MOMENTS variance estimation calibration approach stratified random sampling 

分 类 号：O17[理学—数学]