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Problem: Suppose that we have i.i.d. random variables $ X_{1},\dots,X_{n} \sim \text{Gamma}(\alpha,\beta) $, where $ \alpha > 0 $ is known. Find an efficient estimator for $ \beta $.

Recall that the probability density function of the $ \text{Gamma}(\alpha,\beta) $-distribution is given by $$ \forall x > 0: \quad f(x;\alpha,\beta) = \frac{1}{\Gamma(\alpha) \beta^{\alpha}} \cdot x^{\alpha - 1} e^{- x/\beta}. $$

I am a little lost, but I am guessing that I need to find the Cramér-Rao Lower Bound (CRLB), look for an unbiased estimator and then compare it to the CRLB. Any help would be greatly appreciated.

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There are two formulations for $\Gamma(\alpha,\beta)$, the $\beta$ in one of them being the inverse in the other. When working with gamma distributions, you should always specify which formulation you are working with. –  Learner Feb 10 '13 at 7:11
    
Thanks for the advice. I added the the equation defined as I have been using it. –  user45185 Feb 11 '13 at 5:11
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Dear user45185, I have done some touch-up to your post in order to make it look more appealing to the community. One more thing. You may want to do something about your accept rate. I noticed that the level of response to your posts has been pretty low. If you want to remedy the situation, please work on improving your accept rate. :) –  Haskell Curry Feb 11 '13 at 7:45
    
Thanks a lot. I will take note of that for my previous posts –  user45185 Feb 11 '13 at 7:50
    
The efficiency of an estimator depends precisely on the Cramér-Rao Lower Bound. You may have confused the concept of ‘efficient estimator’ with that of ‘minimum-variance unbiased estimator’. –  Haskell Curry Feb 11 '13 at 9:38
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1 Answer

up vote 1 down vote accepted

The following is a solution, presented in the spirit of rigorous measure-theoretic statistics.


Fix $ \alpha > 0 $. We are considering the one-parameter family $ (P_{\beta})_{\beta > 0} $ of probability distributions, where each $ P_{\beta} $ is the $ \text{Gamma}(\alpha,\beta) $-distribution. According to this webpage, an efficient estimator for $ (P_{\beta})_{\beta > 0} $ using a random sample of size $ n $ is the following Borel-measurable function $ T: \mathbb{R}^{n} \to \mathbb{R} $: $$ \forall (x_{1},\ldots,x_{n}) \in \mathbb{R}^{n}: \quad T(x_{1},\ldots,x_{n}) \stackrel{\text{def}}{=} \frac{1}{n \alpha} \sum_{k=1}^{n} x_{k}. $$ To prove that $ T $ is an efficient estimator for $ (P_{\beta})_{\beta > 0} $, we need to do the following:

  • Prove that $ T $ is an unbiased estimator for $ (P_{\beta})_{\beta > 0} $: $$ \forall \beta > 0: \quad X_{1},\ldots,X_{n} \sim P_{\beta} ~~ \Longrightarrow ~~ \text{E} \left[ T(X_{1},\ldots,X_{n}) \right] = \text{E} \left[ \frac{1}{n \alpha} \sum_{k=1}^{n} X_{k} \right] = \beta. $$

  • Derive the Cramér-Rao Inequality for $ T $ with respect to $ (P_{\beta})_{\beta > 0} $: $$ \forall \beta > 0: \quad X_{1},\ldots,X_{n} \sim P_{\beta} ~~ \Longrightarrow ~~ \text{Var} \left[ T(X_{1},\ldots,X_{n}) \right] \geq \frac{\beta^{2}}{n \alpha}. $$

  • Prove that equality is actually attained by $ T $ in the inequality above: $$ \text{Var} \left[ T(X_{1},\ldots,X_{n}) \right] = \frac{\beta^{2}}{n \alpha}. $$


Conclusion: $ T $ is an efficient estimator for $ (P_{\beta})_{\beta > 0} $ using a random sample of size $ n $.

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