Abstract

Missing data often complicates survey practitioners to construct reliable estimates of the required population parameters. Remembering this fact and motivated with the recent work, this article deals with some imputation methods to handle the missing data problem at the beginning of the analysis and some estimation procedures of the current population mean have been proposed in two-occasion successive sampling. The expressions for the bias, mean squared error and optimum mean squared error are derived using the concept of large sample approximations. The empirical performances are shown over the similar type of estimators designated for the complete response situation and over recently developed estimator which are defined for the situation when missing observations are observed in the sample data. Suitable recommendations have been made for survey researchers.

摘要

缺失数据通常会使调查从业者难以构建所需人口参数的可靠估计。记住这一事实并受最近工作的启发，本文在分析开始时处理了一些用于处理缺失数据问题的插补方法，并在两次连续抽样中提出了当前总体均值的一些估计程序。偏差、均方误差和最佳均方误差的表达式是使用大样本近似的概念推导出来的。经验表现显示在为完整响应情况指定的类似类型的估计器和最近开发的估计器上，这些估计器是为在样本数据中观察到缺失观察的情况定义的。