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【抽样调查】三阶段抽样统计量性质

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标签:计算   样本   mat   个数   公式   统计   相等   math   估计   

三阶段抽样

基本公式

现用\(\mathbb{E}_3,\mathbb{D}_3\)表示在固定初级单元、二级单元时,对第三阶段抽样求均值和方差;\(\mathbb{E}_2,\mathbb{D}_2\)表示在固定初级单元时,对第二阶段求均值和方差;\(\mathbb{E}_1,\mathbb{D}_1\)表示对初级单元求均值和方差。显然有

\[\mathbb{E}(\hat\theta)=\mathbb{E}_1\mathbb{E}_2\mathbb{E}_3(\hat\theta). \]

于是

\[\begin{aligned} \mathbb{D}(\hat\theta)&=\mathbb{E}(\hat\theta^2)-[\mathbb{E}(\hat\theta)]^2\&=\mathbb{E}_1\mathbb{E}_2\mathbb{E}_3(\hat\theta^2)-\{\mathbb{E}_1\mathbb{E}_2\mathbb{E}_3(\hat\theta) \}^2\&=\mathbb{E}_1\mathbb{E}_2\mathbb{E}_3(\hat\theta^2)-\{\mathbb{E}_1[\mathbb{E}_2\mathbb{E}_3(\hat\theta)]^2- \mathbb{D}_1[\mathbb{E}_2\mathbb{E}_3(\hat\theta)]\} \&=\mathbb{D}_1\mathbb{E}_2\mathbb{E}_3(\hat\theta)+\mathbb{E}_1\mathbb{E}_2\mathbb{E}_3(\hat\theta^2)-\mathbb{E}_1[\mathbb{E}_2\mathbb{E}_3(\hat\theta)]^2\&=\mathbb{D}_1\mathbb{E}_2\mathbb{E}_3(\hat\theta)+\mathbb{E}_1[\mathbb{E}_2\mathbb{E}_3(\hat\theta^2)-[\mathbb{E}_2\mathbb{E}_3(\hat\theta)]^2] \end{aligned} \]

注意到\(\mathbb{E}_2\mathbb{E}_3(\hat\theta^2)-[\mathbb{E}_2\mathbb{E}_3(\hat\theta)]^2\)实际上是固定初级单元时,对后面两个单元合作抽样的方差,所以

\[\mathbb{E}_2\mathbb{E}_3(\hat\theta^2)-[\mathbb{E}_2\mathbb{E}_3(\hat\theta)]^2=\mathbb{D}_2\mathbb{E}_3(\hat\theta)+\mathbb{E}_2\mathbb{D}_3(\hat\theta), \]

\[\mathbb{D}(\hat\theta)=\mathbb{D}_1\mathbb{E}_2\mathbb{E}_3(\hat\theta)+\mathbb{E}_1\mathbb{D}_2\mathbb{E}_3(\hat\theta)+\mathbb{E}_1\mathbb{E}_2\mathbb{D}_3(\hat\theta). \]

等概率三阶段抽样

考虑初级单元中二级单元个数相等,二级单元中三级单元个数相等的情形。第一阶段从包含\(N\)个初级单元的总体中以简单随机抽样方式抽取\(n\)个初级单元;第二阶段从包含\(M\)个二级单元的总体中以简单随机抽样方式抽取\(m\)个二级单元;第三阶段从包含\(L\)个三级单元的总体中以简单随机抽样方式抽取\(l\)个三级单元。

对总体均值的估计为

\[\hat{\bar{\bar {\bar Y}}}=\bar{\bar{\bar y}}=\frac{1}{nml}\sum_{i=1}^{n}\sum_{j=1}^{m}\sum_{k=1}^{l}y_{ijk}. \]

它是无偏的。

此时\(\mathbb{E}(\bar{\bar {\bar y}})=\mathbb{E}_1\mathbb{E}_2\mathbb{E}_3(\bar{\bar {\bar y}})\),且三个阶段均是简单随机抽样。有

\[\begin{aligned} \mathbb{E}(\bar{\bar {\bar y}})&=\mathbb{E}_1\mathbb{E}_2\mathbb{E}_3(\bar{\bar {\bar y}})\&=\mathbb{E}_1\mathbb{E}_2\mathbb{E}_3\left(\frac{1}{nm}\sum_{i=1}^{n}\sum_{j=1}^{m}\bar{\bar y}_{ij} \right)\&=\mathbb{E}_1\mathbb{E}_2\left(\frac{1}{nm}\sum_{i=1}^{n}\sum_{j=1}^{m}\bar{\bar Y}_{ij} \right)\&=\frac{1}{nm}\mathbb{E}_1\left[\sum_{i=1}^{n}\mathbb{E}_2\left(\sum_{j=1}^{m}\bar{\bar Y}_{ij} \right)\right] \end{aligned} \]

此处,\(\displaystyle{\sum_{j=1}^{m}\bar{\bar Y}_{ij}}\)是第二阶段简单随机抽样(将二级单元的总体均值视为抽样单元)的样本总值,故\(\displaystyle{\mathbb{E}_2\left(\frac{1}{m}\sum_{j=1}^{m}\bar{\bar Y}_{ij} \right)=\bar {\bar Y}_{i}}\);接下来,\(\displaystyle{\sum_{i=1}^{n}\bar{\bar Y}_{i}}\)是第一阶段简单随机抽样(将一级单元总体均值视为抽样单元)的样本总值,故\(\displaystyle{\mathbb{E}_1\left(\frac{1}{n}\sum_{i=1}^{n}\bar{\bar Y_i} \right)}=\bar{\bar{\bar{Y}}}\)。从而

\[\mathbb{E}(\bar{\bar {\bar y}})=\bar{\bar{\bar Y}}. \]

下计算其方差,先给出几个记号:

\[S_1^2=\frac{1}{N-1}\sum_{i=1}^{N}(\bar{\bar Y}_i-\bar{\bar{\bar Y}})^2,\S_{2i}^2=\frac{1}{M-1}\sum_{j=1}^{M}(\bar{Y}_{ij}-\bar{\bar Y}_i)^2,\quad S_{2}^2=\frac{1}{N}\sum_{i=1}^{N}S_{2i}^2, \S_{3ij}^2=\frac{1}{L-1}\sum_{k=1}^{L}(Y_{ijk}-\bar{Y}_{ij})^2,\quad S_{3}^2=\frac{1}{NM}\sum_{i=1}^{N}\sum_{j=1}^{M}S_{3ij}^2. \]

此时

\[\mathbb{D}(\bar{\bar{\bar y}})=\frac{1-f_1}{n}S_1^2+\frac{1-f_2}{nm}S_2^2+\frac{1-f_3}{nml}S_3^2. \]

\[\mathbb{D}(\bar{\bar{\bar y}})=\mathbb{E}_1\mathbb{E}_2\mathbb{D}_3(\bar{\bar{\bar y}})+\mathbb{E}_1\mathbb{D}_2\mathbb{E}_3(\bar{\bar{\bar y}})+\mathbb{D}_1\mathbb{E}_2\mathbb{E}_3(\bar{\bar{\bar y}}). \]

逐项计算。

计算第一项要用到此结果:

\[\displaystyle{\mathbb{D}_3(\bar{\bar y}_{ij})=\frac{1-f_3}{l}\frac{1}{L-1}\sum_{k=1}^{L}(Y_{ijk}-\bar{\bar{Y}}_{ij})^2}, \]

不妨记\(\displaystyle{\frac{1}{L-1}\sum_{k=1}^{L}(Y_{ijk}-\bar{\bar Y}_{ij})^2}=S_{3ij}^2\),并记\(\displaystyle{S_3^2=\frac{1}{NM}\sum_{i=1}^{N}\sum_{j=1}^{M}S_{3ij}^2}\),则

\[\begin{aligned} \mathbb{E}_1\mathbb{E}_2\mathbb{D}_3(\bar{\bar{\bar y}})&=\mathbb{E}_1\mathbb{E}_2\mathbb{D}_3\left(\frac{1}{nm}\sum_{i=1}^{n}\sum_{j=1}^{m}\bar{ y}_{ij} \right)\&=\frac{1}{n^2m^2}\mathbb{E}_1\mathbb{E}_2\sum_{i=1}^{n}\sum_{j=1}^{m}\mathbb{D}_3(\bar{ y}_{ij})\&=\frac{1-f_3}{nml}\mathbb{E}_1\mathbb{E}_2\left(\frac{1}{nm}\sum_{i=1}^{n}\sum_{j=1}^{m}S_{3ij}^2 \right)\&=\frac{1-f_3}{nml}\frac{1}{NM}\sum_{i=1}^{N}\sum_{j=1}^{M}S_{3ij}^2\&=\frac{1-f_3}{nml}S_3^2. \end{aligned} \]

计算第二项要用到此结果:

\[\mathbb{D}_2\left(\frac{1}{m}\sum_{j=1}^{m}\bar { Y}_{ij} \right)=\frac{1-f_2}{m}\frac{1}{1-M}\sum_{j=1}^{M}(\bar{ Y}_{ij}-\bar{\bar Y}_i)^2 \]

不妨记\(\displaystyle{\frac{1}{1-M}\sum_{j=1}^{M}(\bar{Y}_{ij}-\bar{\bar Y}_i)^2=S_{2i}^2}\),并记\(\displaystyle{S_2^2=\frac{1}{N}\sum_{i=1}^{N}S_{2i}^2}\),则第二项为

\[\begin{aligned} \mathbb{E}_1\mathbb{D}_2\mathbb{E}_3(\bar{\bar{\bar y}})&=\mathbb{E}_1\mathbb{D}_2\left(\frac{1}{nm}\sum_{i=1}^{n}\sum_{j=1}^{m}\bar{ Y}_{ij} \right)\&=\frac{1}{n^2}\mathbb{E}_1\left[\sum_{i=1}^{n}\mathbb{D}_2\left(\frac{1}{m}\sum_{j=1}^{m}\bar{ Y}_{ij} \right)\right]\&=\frac{1}{n^2}\mathbb{E}_1\left(\sum_{i=1}^{n}\frac{1-f_2}{m}S_{2i}^2 \right)\&=\frac{1-f_2}{nm}\mathbb{E}_1\left(\frac{1}{n}\sum_{i=1}^{n}S_{2i}^2 \right)\&=\frac{1-f_2}{nm}\frac{1}{N}\sum_{i=1}^{N}S_{2i}^2\&=\frac{1-f_2}{nm}S_{2}^2. \end{aligned} \]

\(\displaystyle{S_1^2=\frac{1}{N-1}\sum_{i=1}^{N}(\bar{\bar Y}_i-\bar{\bar{\bar Y}})^2}\),则第三项为

\[\begin{aligned} \mathbb{D}_1\mathbb{E}_2\mathbb{E}_3(\bar{\bar{\bar y}})&=\mathbb{D}_1\left(\frac{1}{n}\sum_{i=1}^{n}\bar{\bar Y}_i \right)\&=\frac{1-f_1}{n}\frac{1}{N-1}\sum_{i=1}^{N}(\bar{\bar Y}_i-\bar{\bar{\bar Y}})^2\&=\frac{1-f_1}{n}S_1^2. \end{aligned} \]

【抽样调查】三阶段抽样统计量性质

标签:计算   样本   mat   个数   公式   统计   相等   math   估计   

原文地址:https://www.cnblogs.com/jy333/p/sampling_survey_5.html

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