Finding more information after finding MLE with Indicator functions.

Ex: $$X_1 , X_2 , ... , X_n$$ ~ $$U(-\theta, \theta); f(x; \theta) = \frac{1}{2\theta}; -\theta \leq X \leq \theta; \theta > 0$$

I believe this is the correct approach to finding the MLE in this particular case, but the only method I've used before is when differentiation is valid and I've never worked with indicator functions before so please bear with me and also feel free to comment any corrections for this portion of the post.

First I have,

$$L = (X_i ; \theta) = ((X_1, X_2, ... , X_n); \theta)$$

$$= \prod_{i = 1}^{n} f(x; \theta) = \prod_{i = 1}^{n} \frac{1}{2\theta}$$

Taking the derivative of the log of this likelihood function isn't valid since it would lead to the conclusion that $$\frac{-n}{2\theta} = 0$$, which isn't helpful in finding the MLE $$\hat{\theta}$$ of $$\theta$$.

So I can use the Indicator function $$I(-\theta \leq X_i \leq \theta), (\theta > 0)$$

The likelihood function is thus

$$L(X;\theta) = \frac{1}{(2\theta)^n}I(-\theta \leq X_{min})I(X_{max} \leq \theta)$$

Since the limit $$(\theta > 0)$$ exist here, then $$L(\theta) = 0$$ when $$\theta \leq 0$$

Thus the likelihood function would be $$L(\theta) \propto \frac{1}{(2\theta)^n}I(X_{max} \leq \theta)$$

While recognising that $$L(\theta)$$ is a decreasing function and the presence of the limit $$(X_{max} \leq \theta)$$, if we want to maximise $$L(\theta)$$, then $$\theta$$ must be as small as possible.

Therefore,

$$\hat{\theta}_{MLE} = X_{max}$$

Provided that everything above is correct (or corrected in comments and answers), the question I have is about how to use this information for further steps of determining things like $$Var(\hat{\theta})$$, $$Bias(\hat{\theta})$$, $$MSE(\hat{\theta})$$, etc.?

• Also please assume that I do not know how to find the MLE in an approach that is not the derivative approach since that is all that my professor covered. Everything I wrote here, I learned from outside sources including stackexchange posts relevant to this topic. I also don't know anything about indicator functions in general since I've never encountered them before in lectures, I only know what I have seen from outside sources. So if there's any fundamental misunderstandings here, please correct me and help me understand what's actually happening. Thank you! – LegendOfKass Mar 18 at 18:38
• The MLE of course is derived in detail here and here. Where are you stuck while finding the variance/bias/MSE of the MLE? The sampling distribution of $\max (X_1,\ldots,X_n)$ is well-known. You can use that if you want to find all three. – StubbornAtom Mar 18 at 19:31

One thing to watch for, since you didn't clearly indicate it: the MLE is the maximum of the absolute values $$|X_k|$$, not simply the maximum of the $$X_k$$.

Taking the derivative of the log of this likelihood function isn't valid since it would lead to the conclusion that $$\frac{-n}{2\theta} = 0$$, which isn't helpful in finding the MLE $$\hat{\theta}$$ of $$\theta$$.

Let's step back a bit. Instead of blindly saying "take the derivative, set it equal to zero", let's look at the problem we're trying to solve with it. We're trying to find a maximum - and if you recall from your calculus class, the standard way to find a maximum is to look for critical points. The zeros of the derivative are critical points, but they're not the only critical points. In addition to them, we also have to consider any points at which the derivative fails to exist, and the boundary of the domain.

What does that give us here? As you already found, the derivative doesn't have any zeros. The domain for $$\theta$$ is $$(0,\infty)$$; as $$\theta\to\infty$$, the probability goes to zero so that's not the maximum. As $$\theta\to 0$$, our formula $$(2\theta)^{-n}$$ goes to $$\infty$$. That seems unlikely; there must be a problem here that means the formula doesn't apply. Before we go any farther, we need to resolve this issue.

Resolving this issue is simple in principle; the formula $$f(x;\theta)=\frac1{2\theta}$$ is only valid for $$|x|\le \theta$$, and the probability density is zero if $$|x|>\theta$$. There's more than one way to write this down - both the piecewise definition $$f(x;\theta) = \begin{cases}\frac1{2\theta}& -\theta\le x\le\theta\\ 0&\text{otherwise}\end{cases}$$ and the indicator function form $$f(x;\theta)=\frac1{2\theta}\mathbf{1}_{[-\theta,\theta]}(x)$$ are valid.

So then, the problem with the density at zero is resolved. Once we've gone closer in than some of the observed $$x$$-values, some of the $$f(X_k;\theta)$$ we're putting into the product are zero and thus the whole product is zero.

Well, that's the boundary dealt with, and we still haven't found anywhere that could be a maximum. There's only one possible critical point left to try: places where the derivative fails to exist. The pieces that go into our density function are smooth, so the only way the derivative can fail to exist is if we switch formulas. That happens when $$|X_k|=\theta$$ for some $$k$$. Looking closer, if $$\theta=|X_k|$$ and $$|X_k|<|X_j|$$ for some other $$j$$, the term $$f(X_j;\theta)$$ will be zero in an interval around $$\theta$$; that rules out $$|X_k|$$ as a potential minimum.

The only option left, then, is $$\hat{\theta}=\max_k |X_k|$$, the largest of them. The density function jumps from zero to $$(2\hat{\theta})^{-n}$$ there, and then decays as $$\theta$$ continues to increase.

... the question I have is about how to use this information for further steps of determining things like variance and bias of $$\hat{\theta}$$.

Now that we have our estimator, we shift perspectives. This is no longer a parameter estimation problem; it's a problem of finding things out about a known probability distribution. Given a fixed $$\theta$$ and $$n$$, what does $$\hat{\theta}$$ look like?

This distribution is pretty well known; it's an order statistic. The easiest handle we can get is the cumulative probability distribution: $$\hat{\theta} \le x$$ if and only if $$|X_k|\le x$$ for each $$k$$. Those events are independent and each has probability $$\frac{x}{\theta}$$ (for $$0\le x\le\theta$$), so the cumulative distribution function is $$G(x) =P(\hat{\theta}\le x) = \begin{cases} 0 & x\le 0\\ \frac{x^n}{\theta^n}& 0\le x\le \theta\\ 1& x\ge\theta\end{cases}$$ Now that we have a cumulative distribution function $$G$$, we can differentiate it to find the density $$g(x)$$. Then things like the mean $$E(\hat{\theta})=\int_{\mathbb{R}}xg(x)$$, the variance $$E(\hat{\theta}^2)-(E(\hat{\theta}))^2$$, and other measures are routine calculations.

• Thank you! This was very helpful! I do have some questions. When you were talking about the domains and limits, you said it seemed unlikely that $(2\theta)^-n$ goes to infinity while $\theta$ approaches 0 - I'm not sure why this would seem unlikely? Towards the end, how does $\hat{\theta} \leq x$ make sense? since x must be less than or equal to theta. Lastly, how did you determine the probability x/theta? is it simply the x value multiplied by the pdf of U(0, theta), which is x/(theta - 0)? Everything else makes sense, just struggling to see how it connects at these 3 points of the process. – LegendOfKass Mar 18 at 20:45
• That $(2\theta)^{-n}$ goes to $\infty$ there? Not surprising - that's just a fact. The part that we should be skeptical of is that it's the likelihood function. Saying that $\theta=0$ is the most likely choice no matter what the observations are is just not plausible here. – jmerry Mar 18 at 20:46
• How does $\hat{\theta} < x$ make sense here? $\hat{\theta}$ is our estimator function, the maximum of the $|X_n|$. It's not the same as $\theta$, which is just a fixed number at this point. – jmerry Mar 18 at 20:48
• How did I determine the probability $x/\theta$? It's the probability that some $X_k$, drawn from this distribution with density $1/(2\theta)$ on $[-\theta,\theta]$, is in the interval $[-x,x]$. That's $\int_{-x}^x 1/(2\theta)\,dt = 2x/(2\theta)=x/\theta$. – jmerry Mar 18 at 20:51
• So saying that $\theta = 0$ is not plausible because of the domain $(\theta > 0)$? And for the $(\hat{\theta} \leq x)$, this works because it's simply saying that in the interval $[-\theta, \theta]$ which can include any number of x values distributed within it, there is a $\hat{\theta}$ MLE function that is somewhere between the $X_{max}$ and the domain bounds $[-\theta, \theta]$? I understand now what's happening with the probability and cdf parts. – LegendOfKass Mar 18 at 21:02