Posted on Categories:Survey sampling, 抽样调查, 统计代写, 统计代考

# 统计代写|抽样调查代考Survey sampling代写|STA311 Randomized Response Techniques

## avatest™帮您通过考试

avatest™的各个学科专家已帮了学生顺利通过达上千场考试。我们保证您快速准时完成各时长和类型的考试，包括in class、take home、online、proctor。写手整理各样的资源来或按照您学校的资料教您，创造模拟试题，提供所有的问题例子，以保证您在真实考试中取得的通过率是85%以上。如果您有即将到来的每周、季考、期中或期末考试，我们都能帮助您！

•最快12小时交付

•200+ 英语母语导师

•70分以下全额退款

## 统计代写|抽样调查代考Survey sampling代写|Randomized Response Techniques

Warner (1965) gave us $50^{+}$years back his novel ‘Randomized Response (RR) Technique (RRT)’ to procure trustworthy data on sensitive personal items from sampled people from a community, like his/her proneness to tax evasion, illegal driving habits, gambling involvement etc., namely the features people usually like to hide from others.

His device is to present before a chosen respondent a box of identical cards in proportions $p\left(0<p \neq \frac{1}{2}<1\right)$ bearing $A$ and $(1-p)$ bearing $A^{C}$, the complementary characteristic. The respondent is to randomly draw a card from the box and return to it after telling the interviewer ‘Yes’ if the card mark matched his/her trait $A$ or $A^{C}$ and ‘No’ if it did not match. Other chosen persons are also to independently repeat this exercise, not of course divulging the card label to the interviewer. Warner (1965) selected samples by simple random sampling with replacement (SRSWR) and easily provided appropriate estimator for the unknown proportion $\theta$ of people bearing $A$ in the community along with an appropriate variance estimator. Chaudhuri $(2001,2011 \mathrm{a}, 2016)$ propagated the view that an RRT has only to elicit a truthful response to a query on implementing an RR trial from a respondent no matter how chosen and provided (i) every sampled person is given a positive inclusion-probability and (ii) every pair of distinct respondents is given a positive inclusion-probability; then (a) an unbiased estimator for $\theta$ along with an (b) unbiased estimator of the variance thereof is available. Let us show how.

Let $U=(1, \cdots i, \cdots, N)$ denote a known collection of people in a community identified and labeled $i=1, \cdots, N$ with values $y_{i}$ such that
\begin{aligned} y_{i} &=1 & & \text { if } i \text { bears } A \ &=0 & & \text { if } i \text { bears } A^{C} \end{aligned}
and our intention is to estimate $Y=\sum_{1}^{N} y_{i}$ and $\theta=\frac{Y}{N}$ on obtaining by Warner’s $\mathrm{RR}$ method a response from a sampled person $i$ the response
\begin{aligned} I_{i} &=1 \quad \text { if for } i \text { the card-type matches the feature } A \text { or } A^{C} \ &=0, \quad \text { if it mis-matches. } \end{aligned}

## 统计代写|抽样调查代考Survey sampling代写|Inverse Bernoullian Trial Approach

An alternative to Warner’s (1965) approach is given by Singh and Grewal (2013) where a sampled person $i$ instead of reporting ‘Yes’ or ‘No’ on performing a respective Bernoullian trial on choosing an ‘ $A$ ‘ or ‘ $A{ }^{C}$ ‘ – marked on each draw with replacement is advised to go on drawing randomly and independently a card from the box with replacement and stop the exercise the first time a ‘match’ between the card-type and the trait occurs and tell the interviewer the ‘trial number’ on which this happens. With this inverse Bernoullian trial system the theory of estimation changes drastically. For SRSWR and varying probability sampling situations the results are presented by Chaudhuri and Dihidar (2014).

Singh and Grewal’s (2013) amendment to Warner’s (1965) RRT demands from an $i$ th respondent the report of the value $g_{i}$ the draw-number on which the first match of the card-type and the trait takes place. For this inverseBernoullian or negative binomial trial-distribution from Walpole and Myers (1993) one may gather
\begin{aligned} &E_{R}\left(g_{i}\right)=\frac{y_{i}}{p}+\frac{1-y_{i}}{1-p} \text { and } \ &V_{R}\left(g_{i}\right)=y_{i}\left(\frac{1-p}{p^{2}}\right)+\left(1-y_{i}\right)\left(\frac{p}{(1-p)^{2}}\right), i \in U . \end{aligned}

# 抽样调查代写

## 统计代写|抽样调查代考Survey sampling代写|Inverse Bernoullian Trial Approach

Singh 和 Grewal (2013) 给出了 Warner (1965) 方法的替代方案，其中一个被抽样的人 $i$ 而不是在执行相应的伯努利试验时报告 第一次出现“匹配”时停止练习，并告诉面试官“发生这种情况的试用号。有了这个逆伯努利试验系统，估计理论发生了巨大变化。 对于 SRSWR 和不同概率抽样情况，Chaudhuri 和 Dihidar (2014) 给出了結果。

Singh 和 Grewal (2013) 对 Warner (1965) RRT 要求的修正 $i$ 被诉人的价值报告 $g_{i}$ 卡粂型和特征的第一次匹配发生的抽奖号码。 对于 Walpole 和 Myers (1993) 的这个逆伯努利或负二项式试验分布，人们可能会收集
$$E_{R}\left(g_{i}\right)=\frac{y_{i}}{p}+\frac{1-y_{i}}{1-p} \text { and } \quad V_{R}\left(g_{i}\right)=y_{i}\left(\frac{1-p}{p^{2}}\right)+\left(1-y_{i}\right)\left(\frac{p}{(1-p)^{2}}\right), i \in U$$

avatest.org 为您提供可靠及专业的论文代写服务以便帮助您完成您学术上的需求，让您重新掌握您的人生。我们将尽力给您提供完美的论文，并且保证质量以及准时交稿。除了承诺的奉献精神，我们的专业写手、研究人员和校对员都经过非常严格的招聘流程。所有写手都必须证明自己的分析和沟通能力以及英文水平，并通过由我们的资深研究人员和校对员组织的面试。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。