Calibration Approach to Developed Triple Means Estimators

Abstract

Calibration estimation has become a ubiquitous methodology across diverse fields, providing a foundational framework for tackling complex statistical problems. Its significance In recent years, has emerged as a pivotal topic in research on estimation in survey sampling where it has emerged as a crucial area of study. By providing a systematic approach to integrating auxiliary information, calibration enhances the estimation procedure, rendering it a valuable tool in statistical analysis. The article propounds an calibration approaches of triple mean under simple random sampling of variance estimators, the proposed calibration have been develop utilizing sample variance incorporation with existing estimators of AM, GM, HM, in the problem constraints of the optimization in other to contribute effectively to new design calibrated weight. However, the proposed new weight is obtained using most common approach Lagrange function with two multipliers. The motivation for using calibration scheme is due to their ability in reduce bias and means square error, enhance precision, base on how auxiliary variable are been utilized, provide flexibility, comply with standard and improve decision making. Focusing on simulated data approached using exponential and beta the result indicate superiority of classes calibrated estimators of studied via R packages.