优化的可选随机应变技术

IF 1.7 3区数学 Q1 STATISTICS & PROBABILITY International Statistical Review Pub Date : 2024-05-29 DOI:10.1111/insr.12581

Kavya Pushadapu, Sarjinder Singh, Stephen A. Sedory

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引用次数: 0

摘要

摘要在本文中，我们首先回顾了 Chaudhuri 和 Mukerjee 开发的可选随机响应技术估计器 (ORRTE)，用于估计人口中敏感特征的比例。我们证明，他们的估计器是无偏的，方差小于华纳估计器。然后，我们尝试开发一种优化的可选随机响应技术估计器（OORRTE）。结果表明，建议的 OORRTE 比 ORRTE 更有效。我们讨论了模拟研究的结果，并对各种情况进行了解释。根据 Ulrich、Schroter、Striegel 和 Simon 引入的功率分析，计算了 Warner 估计器、ORRTE 和 OORRTE 的样本大小。最后，我们将 COVID-19 视为部分敏感变量（即对某些变量敏感，但对另一些变量不敏感），并将其应用于真实数据中。所用数据包含在论文中，模拟研究中使用的 R 代码记录在在线资料中。

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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An Optimised Optional Randomised Response Technique

SummaryIn this paper, we begin by reviewing the optional randomised response technique estimator (ORRTE) developed by Chaudhuri and Mukerjee for estimating the proportion of a sensitive characteristic in a population. We show that their estimator is unbiased and has smaller variance than the Warner's estimator. Then we make an attempt at developing an optimised optional randomised response technique estimator (OORRTE). The proposed OORRTE is shown to be more efficient than the ORRTE. Findings from simulation studies are discussed and interpreted for various situations. Sample sizes for the Warner's estimator, the ORRTE and the OORRTE are computed based on power analysis introduced by Ulrich, Schroter, Striegel and Simon. Finally, we include an application to real data on COVID‐19 by considering it to be partially sensitive variable; that is, sensitive to some but not to others. The data used are included in the paper and the R‐codes used in the simulation study are documented in online material.

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来源期刊

International Statistical Review 数学-统计学与概率论

CiteScore

4.30

自引率

5.00%

发文量

审稿时长

>12 weeks

期刊介绍： International Statistical Review is the flagship journal of the International Statistical Institute (ISI) and of its family of Associations. It publishes papers of broad and general interest in statistics and probability. The term Review is to be interpreted broadly. The types of papers that are suitable for publication include (but are not limited to) the following: reviews/surveys of significant developments in theory, methodology, statistical computing and graphics, statistical education, and application areas; tutorials on important topics; expository papers on emerging areas of research or application; papers describing new developments and/or challenges in relevant areas; papers addressing foundational issues; papers on the history of statistics and probability; white papers on topics of importance to the profession or society; and historical assessment of seminal papers in the field and their impact.