Estimation with incomplete frames using varying probability sampling with replacement

Q4 Mathematics

Model Assisted Statistics and Applications Pub Date : 2023-03-24 DOI:10.3233/mas-221379

Jyoti, Sarla Pareek, P. C. Gupta

引用次数: 0

Abstract

Sampling frames are mostly incomplete in large scale surveys. This paper suggests the use of probability proportional to size with replacement (PPSWR) sampling scheme for estimation of population mean with an incomplete frame. The variance of the estimators has been obtained and its efficiency has been compared with Agarwal and Gupta (2008) estimator when the frame is not complete. The results obtained have been illustrated with the help of hypothetical data. The problem of determining optimum sample size and retainment factor has also been discussed using a suitable linear cost function.

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具有替换的变概率采样的不完全帧估计

在大规模调查中，抽样框架大多是不完整的。本文建议使用与替换大小成比例的概率（PPSWR）抽样方案来估计不完全框架下的总体平均值。得到了估计量的方差，并与Agarwal和Gupta（2008）估计量在帧不完全时的效率进行了比较。所获得的结果已在假设数据的帮助下加以说明。还讨论了使用合适的线性成本函数确定最佳样本量和保留因子的问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Model Assisted Statistics and Applications Mathematics-Applied Mathematics

CiteScore

1.00

自引率

0.00%

发文量

期刊介绍： Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.