Let X1,, X, denote a random sample of size n from the population with probability density function 16) = {** 38-4, r2 B 0, otherwise where 3> 0 is the population parameter we want to estimate. Consider the estimator B = min(X1,,X„). [The estimator is the 1st-order order statistic of the random sample.] (a) Derive the bias of the estimator 3. (b) Derive the mean squared error of 3, MSE(3).

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Let \( X_1, \ldots, X_n \) denote a random sample of size \( n \) from the population with probability density function

\[
f(x) = 
\begin{cases} 
3\beta^3 x^{-4}, & x \geq \beta \\
0, & \text{otherwise}
\end{cases}
\]

where \( \beta > 0 \) is the population parameter we want to estimate. Consider the estimator \(\hat{\beta} = \min(X_1, \ldots, X_n)\). [The estimator \(\hat{\beta}\) is the 1st-order order statistic of the random sample.]

(a) Derive the bias of the estimator \(\hat{\beta}\).

(b) Derive the mean squared error of \(\hat{\beta}\), \( \text{MSE}(\hat{\beta}) \).
Transcribed Image Text:Let \( X_1, \ldots, X_n \) denote a random sample of size \( n \) from the population with probability density function \[ f(x) = \begin{cases} 3\beta^3 x^{-4}, & x \geq \beta \\ 0, & \text{otherwise} \end{cases} \] where \( \beta > 0 \) is the population parameter we want to estimate. Consider the estimator \(\hat{\beta} = \min(X_1, \ldots, X_n)\). [The estimator \(\hat{\beta}\) is the 1st-order order statistic of the random sample.] (a) Derive the bias of the estimator \(\hat{\beta}\). (b) Derive the mean squared error of \(\hat{\beta}\), \( \text{MSE}(\hat{\beta}) \).
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