# 7.41

Let $X_1, \dots , X_n$ be an random sample from a population with mean $\mu$ and variance $\sigma^2$

## a

Show that the estimator $\sum_{i=1}^{n} a_i X_i$ an unbiased estimator of $\mu$ if $\sum_{i=1}^{n} = 1$.

These $a_i$’s act as a weighted average.

$E\Big( \sum a_i X_i \Big) = \sum E\Big( a_i X_i \Big) = \sum a_i E(X_i) = \sum a_i \mu = \mu \sum a_i = \mu$

## b

Among all unbiased estimators of this form (called linear unbiased estimators) find the one with miminum variance and calculate the variance.

$V\Big( a_i X_i \Big) = \sum a_i^2 V(X_i) = \sum a_i^2 \sigma = \sigma \sum a_i^2$

We want to minimize this variance subject to $\sum a_i = 1$. Notice that the average of $\sum a_i$ is $\frac{ 1 }{ n }$.

There are many ways to do this

1 using the mean

A trick we often use when dealing with sums is to add and subtract the mean.

\begin{align} \sum a_i^2 & = \sum (a_i + 1/n - 1/n)^2 \\ & = \sum a_i^2 + \frac{ 1 }{ n^2 } + \frac{ 2 a_i }{ n } + \frac{ 1 }{ n^2 } - \frac{ 2 a_i}{ n } - \frac{ 2 }{ n^2 } \\ & = \sum a_i^2 - \frac{ 1 }{ n }^2 + \frac{ 1 }{ n^2 } \\ & = \sum (a_i - \frac{ 1 }{ n })^2 + \frac{ 1 }{ n^2 } \end{align}

This is minimized when $a_i = \frac{ 1 }{ n }$. Then,

\begin{align} V\Big(\sum a_i X_i\Big) & = \sigma^2 \sum \frac{ 1 }{ n^2 } = \frac{ \sigma^2 }{ n } \\ \sum a_i X_i & = \sum \frac{ 1 }{ n } X_i = \frac{ 1 }{ n } \sum X_i = \overline{ X } \end{align}

2 using power mean

Recall the power mean inequality. For $k_1 \geq k_2$,

$\Big[ \frac{ \sum a_i^{k_1} }{ n } \Big]^{1/k_1} \geq \Big[ \frac{ \sum a_i^{k_2} }{ n } \Big]^{1/k_2}.$

In our case,

\begin{align} \Big[ \frac{ \sum a_i^{2} }{ n } \Big]^{1/2} & \geq \Big[ \frac{ \sum a_i^{1} }{ n } \Big]^{1} \\ \frac{ \sum a_i^{2} }{ n } & \geq \Big[ \frac{ \sum a_i }{ n } \Big]^{2} \\ \sum a_i^{2} & \geq \frac{(\sum a_i)^2 }{n } \\ & = \frac{ 1 }{ n } \end{align}

This is minimized when they are equal, which is the case for $a_i = \frac{ 1 }{ n }$.

$\sum a_i^2 = \sum \frac{ 1 }{n^2 } = \frac{ n }{ n^2 } = \frac{ 1 }{ n }$

3 Cauchy Inequality

Recall Cauchy’s Inequality.

$\Big( \sum_{k=1}^{n} a_k b_k \Big)^2 \leq \Big( \sum_{k=1}^{n} a_k^2 \Big)\Big( \sum_{k=1}^{n} b_k^2 \Big)$ \begin{align} (\sum a_i)^2 & \leq \sum(a_i^2) \sum(1^2) \\ 1^2 & \leq \sum(a_i^2) n \\ \frac{ 1 }{ n } & \leq \sum(a_i^2) \end{align}

then follow as above to see $a_i = \frac{ 1 }{ n }$.