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We typically introduce maximum likelihood by considering a family of distributions $\{P_\theta: \theta \in \Theta\}$ which admit densities $f_\theta$ with respect to some underlying measure $\lambda$. We observe a random vector $X \sim P_{\theta_0}$ where $\theta_0$ intuitively represents the "true, unknown" value of $\theta$. The maximum likelihood estimator of $\theta_0$ is then defined to be $\hat \theta = \arg \max_\theta f(X|\theta)$.

How do we resolve the fact that $f_\theta$ is only unique almost surely? Normally we write these things off, but it actually matters here. A typical example is $X_1, X_2 \sim \mbox{Uniform}(0, \theta), \theta > 0$. Depending on whether we take $f(x|\theta) \propto I[0 \le x \le \theta]$ or $I[0 < x < \theta]$ affects what the estimator is. If it is the former we have $\hat \theta = \max\{X_1, X_2\}$ whereas with the latter the MLE doesn't exist. They are both valid densities for a Uniform$(0, \theta)$ but provide different answers. It seems natural in this case to prefer $I[0 \le x \le \theta]$ since, in this case, the density is positive and continuous when restricted to the support but I find it distaseful to define things in terms of densities instead of distributions.

My two thoughts for solutions are (1) maybe once we have specified $\{f_\theta: \theta \in \Theta\}$, provided that the MLE exists, it may be the case that it is unique a.s. and (2) maybe we just bite the bullet and take the most natural family of densities for the problem at hand; when we want to make use of theoretical results, we make sure to choose $f_\theta$ so that it satisfies the required regularity conditions.

I'm unsure whether this is better for stats.stackexchange or here since it's statistics but focuses on the mathematical formalism more than statisticians prefer to.

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Typo: $\hat\theta = \max\lbrace X_1, X_2 \rbrace$. Not $\min$. –  Michael Hardy Mar 11 '12 at 23:00
    
Even before this issue, you have another problem (or maybe I'm restating the same problem): densities are only really defined modulo negligible functions -- they don't have well-defined values at any point! I think the resolution for this is the same as for what you're worried about: you really want to take a lim sup or something similar. –  Hurkyl Mar 12 '12 at 1:19

1 Answer 1

I think almost surely (ha ha) we're going to have to think about

  • topological notions: closed sets and continuous functions; and
  • densities.

However, if you want to think seriously about maximum likelihood, do look at this: Le Cam, Lucien (1990). "Maximum likelihood — an introduction". ISI Review 58 (2): 153–171. I think you can find it on the web. It's a somewhat sarcastic article.

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Seventh item there. –  Did Mar 12 '12 at 14:26

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