L-Moments Based Calibrated Variance Estimators Using Double Stratified Sampling  

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

机构地区：[1]Department of Mathematics and Statistics,International Islamic University,Islamabad,44000,Pakistan [2]Department of Mathematics and Statistics,PMAS-Arid Agriculture University,Rawalpindi,46300,Pakistan [3]Department of Mathematics,College of Science,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9期3411-3430,共20页计算机、材料和连续体（英文）

基　　金：The authors thank 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.1/64/42.Ishfaq Ahmad and Ibrahim Mufrah Almanjahie received the grant.

摘　　要：Variance is one of the most vital measures of dispersion widely employed in practical aspects.A commonly used approach for variance estimation is the traditional method of moments that is strongly influenced by the presence of extreme values,and thus its results cannot be relied on.Finding momentum from Koyuncu’s recent work,the present paper focuses first on proposing two classes of variance estimators based on linear moments(L-moments),and then employing them with auxiliary data under double stratified sampling to introduce a new class of calibration variance estimators using important properties of L-moments(L-location,L-cv,L-variance).Three populations are taken into account to assess the efficiency of the new estimators.The first and second populations are concerned with artificial data,and the third populations is concerned with real data.The percentage relative efficiency of the proposed estimators over existing ones is evaluated.In the presence of extreme values,our findings depict the superiority and high efficiency of the proposed classes over traditional classes.Hence,when auxiliary data is available along with extreme values,the proposed classes of estimators may be implemented in an extensive variety of sampling surveys.

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

分 类 号：O17[理学—数学]