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

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## 统计代写|抽样调查代考Survey sampling代写|Optional Randomization

While tackling sensitive issues by dint of RRT’s an experimenter presumes every respondent to treat along with him/her the item to be embarrassing.

But many respondents may differ and regard it not stigmatizing at all and may be quite willing to divulge his/her secret with no hesitation. The same item may however be treated as stigmatizing by others who may be persuaded to give out only RR’s but no DR’s. Facing such situations in actual practice Chaudhuri and Mukerjee $(1985,1988)$ presented their Optional Randomization Response (ORR) technique rather than Compulsory RRT’s or (CRR) techniques. Chaudhuri (2011a) in his Chapters 5 and 7 separately narrate ORR techniques to cover qualitative characteristics and quantitative features respectively. Chaudhuri and Christofides (2013) say more about them. Chaudhuri and Saha (2004) develop Chaudhuri and Mukerjee’s (1985, 1988) approach further covering general sampling schemes leaving aside earlier restriction to SRSWR by applying Rao-Blackwellization. They gather DR’s from a part $s_{1}$ of the full sample $s=\left(s_{1}, s_{2}\right)$ and RR’s from $s_{2}$, the remainder of the complete sample and construct a basic estimator as the sum of the two linear estimators based on DR’s in $s_{1}$ and RR’s in $s_{2}$. Since knowing the DR’s in $s_{1}$ and the RR technique one may consider the basic RR-based estimator from the entire sample $s$ as well. Since the former estimator may be treated as the conditional expectation of the basic estimator given the DR’s in $s_{1}$ the technique of Rao-Blackwellization may be applied to show that the former estimator is better than the latter. Also unbiased variance estimation becomes a simple task.

## 统计代写|抽样调查代考Survey sampling代写|Protection of Privacy

An important issue in RRT is protection of privacy, though the experimenter claims that an RRT is intended to protect the privacy of a respondent even if he/she gives out the truth though not divulging the question to which an answer is truthfully given as a response. However the real trait, with a certain probability may be revealed to the inquirer. A way to study this is of course Bayesian. Presupposing that for a person labeled $i$ there exists an unknown prior probability $L_{i}$ that he/she may bear a sensitive qualitative trait carrying a stigma once a response $R$ is elicited from him/her one is justified to be curious about what happens to the posterior probability that he/she bears one stigmatizing attribute $A$ once, following the specified RRT a response $R$ has been given out truthfully on implementing the prescribed RRT.

Let us illustrate a few. The approaches are as follows for qualitative traits:

For Warner’s RRT: $0<p<1 ; p \neq \frac{1}{2}$.
\begin{aligned} L_{i}(1) &=\frac{L_{i} \operatorname{Prob}\left[I_{i}=1 \mid y_{i}=1\right]}{L_{i} \operatorname{Prob}\left[I_{i}=1 \mid y_{i}=1\right]+\left(1-L_{i}\right) \operatorname{Prob}\left[I_{i}=1 \mid y_{i}=0\right]} \ &=\frac{p L_{i}}{(1-p)+(2 p-1) L_{i}} \ L_{i}(0) &=\frac{(1-p) L_{i}}{p+(1-2 p) L_{i}} \end{aligned}

# 抽样调查代写

## 统计代写|抽样调查代考Survey sampling代写|Protection of Privacy

$$L_{i}(1)=\frac{L_{i} \operatorname{Prob}\left[I_{i}=1 \mid y_{i}=1\right]}{L_{i} \operatorname{Prob}\left[I_{i}=1 \mid y_{i}=1\right]+\left(1-L_{i}\right) \operatorname{Prob}\left[I_{i}=1 \mid y_{i}=0\right]} \quad=\frac{p L_{i}}{(1-p)+(2 p-1) L_{i}} L_{i}(0)=\frac{(1-p) L_{i}}{p+(1-2 p) L_{i}}$$

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## 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。