(a) Find the MLE of 0. (b) Find the MLE of Po(X > 1). (c) Prove that the MLE you find in (b) is a consistent estimator of the probability Po(X > 1).

A First Course in Probability (10th Edition)
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Chapter1: Combinatorial Analysis
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**Problem Statement:**

Suppose \( X_1, \ldots, X_n \) are independent and identically distributed (iid) with probability density function (pdf) 

\[
f(x; \theta) = \frac{1}{\theta} e^{-x/\theta}, \quad 0 < x < \infty,
\]

zero elsewhere.

**Tasks:**

(a) Find the Maximum Likelihood Estimator (MLE) of \(\theta\).

(b) Find the MLE of \( P_\theta(X > 1) \).

(c) Prove that the MLE you find in (b) is a consistent estimator of the probability \( P_\theta(X > 1) \).
Transcribed Image Text:**Problem Statement:** Suppose \( X_1, \ldots, X_n \) are independent and identically distributed (iid) with probability density function (pdf) \[ f(x; \theta) = \frac{1}{\theta} e^{-x/\theta}, \quad 0 < x < \infty, \] zero elsewhere. **Tasks:** (a) Find the Maximum Likelihood Estimator (MLE) of \(\theta\). (b) Find the MLE of \( P_\theta(X > 1) \). (c) Prove that the MLE you find in (b) is a consistent estimator of the probability \( P_\theta(X > 1) \).
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