Checking for Biased/Consistency

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SUMMARY

This discussion focuses on the evaluation of the Method of Moments and Maximum Likelihood Estimators for the parameter $\theta$ from the population density function $$f(x;\theta) = \frac{2}{\theta} x e^{\frac{-x^2}{\theta}}$$ for $$x \geq 0$$ and $\theta > 0$. The Method of Moments estimator is derived as $$\hat{\theta}_1 = \frac{4\bar{X}^2}{\pi}$$, while the Maximum Likelihood Estimator is $$\hat{\theta}_2 = 2\bar{Y}$$, where $$Y = X_i^2$$. The discussion also addresses the need to check for bias and consistency of these estimators, with specific methods outlined for evaluation.

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  • Understanding of statistical estimation methods, specifically Method of Moments and Maximum Likelihood Estimation.
  • Familiarity with probability density functions and their properties.
  • Knowledge of calculus, particularly integration techniques and differentiation.
  • Basic understanding of bias and consistency in statistical estimators.
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  • Learn how to evaluate the bias of an estimator through integration, specifically for $$E[\hat{\theta}_2 - \theta]$$.
  • Research the concept of consistency in estimators, focusing on the probability evaluation $$Prob(|\hat{\theta}_2 - \theta| < \epsilon)$$.
  • Study the implications of using different statistical estimators on parameter estimation accuracy.
  • Explore advanced topics in statistical inference, including asymptotic properties of estimators.
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Statisticians, data analysts, and researchers involved in statistical modeling and estimation, particularly those working with Maximum Likelihood and Method of Moments techniques.

Jmath
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Hello I am trying to check if the Method of Moments and Maximum Likelihood Estimators for parameter $\theta$ from a sample with population density $$f(x;\theta) = \frac 2 \theta x e^{\frac {-x^2}{\theta}} $$
for $$x \geq 0$, $\theta > 0$$ with $\theta$ being unknown.

Taking the first moment of this function I found the Method of Moments estimator to be $$\hat{\theta}_1 = \frac{4\bar X^2}{\pi}$$ and solving for the Maximum Likelihood Estimator the Estimator to be $$\hat{\theta}_2 = 2\bar Y$$ where Y is just square of the Sample X_i, i.e. $$Y = X_i^2$$.

Steps in Solving for Method of Moments:
I took the first moment, i.e.

M_1 = E[x] = $$\int_0^\infty{\frac 2 \theta x^2 e^{\frac {-x^2}{\theta}}dx}$$

Solving this integral with $u$ substitution with $$u = \frac{-x}{2}, du = \frac{-1}{2}, v = e^\frac{x^2}{\theta}, dv = -2xe^\frac{-x^2}{\theta}$$

$$\int_0^\infty{\frac 2 \theta x^2 e^{\frac {-x^2}{\theta}}dx} = [-\frac{xe^\frac{-x^2}{\theta}}{2\theta} - \frac{\sqrt{\pi \theta}}{4}]^\infty_0 = \frac{\sqrt{\pi} {\sqrt{\theta}}}{2}$$

So that $$E[x] = \bar{x} = \frac{\sqrt{\pi} {\sqrt{\theta}}}{2}$ gives the Method of Moments Estimator $\hat{\theta_1} = \frac{4\bar{X}^2}{\pi}$$

Steps in Solving for Maximum Likelihood:

$$lnL(\theta)=(\prod_{i=1}^n\frac 2 \theta x e^{\frac {-x^2}{\theta}}) = -n ln((2\theta)) + \sum_{i=1}^nx_i - \frac {1} {\theta} \sum_{i=1}^nx^2_i$$

$$\frac {dlnL(\theta)}{d\theta} = \frac{-n}{2\theta} + \frac{1}{\theta^2} \sum_{i=1}^nx^2_i$$

Setting $\frac {dL(\theta)}{d\theta} = 0$, I found the Maximum Likelihood Estimator $\hat{\theta_2}$ to be $$\hat{\theta_2} = \frac{2\sum_{i=1}^nx^2_i}{n}$ , so that if $Y = X_i^2$ then $\hat{\theta_2} = 2\bar{Y}$$.

I am trying to check if these estimators for $\theta$ from this density function are unbiased and/or consistent but am lost on how to go about doing so, any help would be much appreciated.
 
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To check for bias, evaluate ##E[\hat\theta_2-\theta]## by integrating. The estimate is unbiased iff this evaluates to zero.

To check for consistency, evaluate ##Prob(|\hat\theta_2-\theta|<\epsilon)##. If the result is a function that goes to zero as ##\epsilon\to 0## the estimator is consistent.

A few points by the by:
  • on physicsforums the $ delimiter for latex in-line maths is not recognised. That's why the formatting is all mucked up in places above. Use a double-# instead.
  • your estimates from the two methods are the same, as ##2\bar Y##. Is that what you intended?
  • the statement ##Y=X_i{}^2## occurs twice. This should be ##Y_i=X_i{}^2## as both sides depend on ##i##
By the way, you seem to conclude
 

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