An Efficient Class of Estimators for the Finite Population Mean in Ranked Set Sampling

In this paper, we propose a class of estimators for estimating the finite population mean of the study variable under Ranked Set Sampling (RSS) when population mean of the auxiliary variable is known. The bias and Mean Squared Error (MSE) of the proposed class of estimators are obtained to first degree of approximation. It is identified that the proposed class of estimators is more efficient as compared to [1] estimator and several other estimators. A simulation study is carried out to judge the performances of the estimators.

Estimation of Population Variance Using the Coefficient of Kurtosis and Median of an Auxiliary Variable under Simple Random Sampling

In this study we have proposed a modified ratio type estimator for population variance of the study variable y under simple random sampling without replacement making use of coefficient of kurtosis and median of an auxiliary variable x. The estimator’s properties have been derived up to first order of Taylor’s series expansion. The efficiency conditions derived theoretically under which the proposed estimator performs better than existing estimators. Empirical studies have been done using real populations to demonstrate the performance of the developed estimator in comparison with the existing estimators. The proposed estimator as illustrated by the empirical studies performs better than the existing estimators under some specified conditions i.e. it has the smallest Mean Squared Error and the highest Percentage Relative Efficiency. The developed estimator therefore is suitable to be applied to situations in which the variable of interest has a positive correlation with the auxiliary variable.

New Class of Almost Unbiased Modified Ratio Cum Product Estimators with Knownparameters of Auxiliary Variables

This manuscript deals with new class of almost unbiased ratio cum product estimators for the estimation of population mean of the study variable by using the known values auxiliary variable. The bias and mean squared error of proposed estimators are obtained. An empirical study is carried out to assess the efficiency of proposed estimators over the existing estimators with the help of some known natural populations and it shows that the proposed estimators are almost unbiased and it perform better than the existing estimators.

基于排序集样本和双辅助变量的比率估计改进方法

基于排序集样本和双辅助变量建立总体均值的改进比率估计量,研究了估计量的偏差和均方误差,并从理论上比较了两种抽样方法下的估计效率,最后借助随机模拟和实际例子分析了比率估计方法的有效性.结果表明,利用双辅助变量的改进比率估计精度明显高于简单随机抽样或者单辅助变量比率估计.

基于辅助变量偏斜系数的分别比估计(英文)

本文在分层随机抽样下通过使用辅助变量的偏斜系数提出了几个总体均值的估计。从理论上比较了提出的新估计和经典分别比估计的均方误差。并且通过一个原始数据的实际应用例子验证了提出估计的有效性。

有辅助信息下总体均值的一类新比估计量

本文在简单随机抽样下,使用辅助变量的信息提出一类总体均值估计量,并导出这类估计量的均方误差。理论上比较了提出估计量与已有估计量的效率,数值上模拟验证了提出估计量在一定条件下的有效性。