L-Moments and Calibration-Based Estimators for Variance Parameter

Abstract

The subject of variance estimation is one of the most important topics in statistics. It has been clarified by many different research studies due to its various applications in the human and natural sciences. Different variance estimators are built based on traditional moments that are especially influenced by the existence of extreme values. In this paper, with the presence of extreme values, we proposed some new calibration estimators for variance based on L-moments under double-stratified random sampling. A simulation study with COVID-19 data is performed to evaluate the efficiency of the proposed estimators. All results indicate that the proposed estimators are often superior and highly efficient compared to the existing traditional estimator.

Estimation of population mean under probability proportional to size sampling with and without measurement errors

“…Using the method of pps sampling, many authors like Rao, 1 Tripathi, 2 Anita and Bahl, 3,22 Patel and Bhatt, 4 Ahmad and Shabbir 5 and Singh et al 6 have suggested improved estimators for estimating different population parameters while Anas et al 7,8 and Shahzad et al 9,10 have proposed calibration based mean and variance estimators using $$$ L $$$‐moments. In order to examine the properties and efficiency of the proposed estimators in terms of mean square error, some of the well‐established forms of suggested estimators under pps sampling have been adopted in this section and their properties have been studied.…”

In Situ Flood Frequency Analysis Used for Water Resource Management in Kelantan River Basin

“…L-moments are widely used in applied research such as civil engineering, meteorology, and hydrology. Among the benefits of L-moments include their capacity to work as a linear function of the data, being less prone to sample variability, being more robust to extreme values or outliers in the data and allowing for more confident inferences about the underlying probability distribution from small samples (Anas et al, 2021). This implies that L-moments are less affected by outliers, and the bias of their small sample estimates is kept to a minimum.…”