Usman Shahzad^1,2,*, Ishfaq Ahmad¹, Ibrahim Mufrah Almanjahie^3,4, Nadia H.Al –Noor⁵

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3411-3430, 2021, DOI:10.32604/cmc.2021.017046

Abstract 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… More >

Usman Shahzad^1,2,*, Ishfaq Ahmad¹, Ibrahim Mufrah Almanjahie^3,4, Nadia H. Al Noor⁵, Muhammad Hanif²

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3013-3028, 2021, DOI:10.32604/cmc.2021.014101

Abstract Variance is one of the most 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, L-scale, L-CV) and auxiliary information. It is demonstrated that the proposed L-Moments based calibration variance estimators are more efficient than… More >