# MLE on a restricted parameter space

Suppose $X_1, \ldots, X_n$ are a random sample from a normal distribution with mean $\theta$ and variance $1$. Find the maximum likelihood estimator of $\theta$, under the restriction that $\theta \geq 0$.

I have found the MLE when there is no restriction on $\theta$.

$$L(\theta|\mathbf{x})=(2\pi)^{-n/2} \text{exp}\Big(-\frac{1}{2}\sum_{i=1}^{n}(x_i-\theta)^2 \Big)$$

Differentiating $L(\theta|\mathbf{x})$ with respect to theta, and setting it equal to zero yields $$\sum_{i=1}^{n}(x_i-\theta)=0 \implies \theta=\frac{1}{n}\sum_{i=1}^{n}x_i =\bar{x}$$ The second derivative is negative, hence the MLE of $\theta$ is $\hat{\theta}=\bar{X}$.

How would I incorporate the restriction $\theta \geq 0$.

• Under the assumption that this is homework (please use the homework tag, if so!) I'll just give a hint. What if $\bar{X} = -1$? What is the constrained MLE then? Why? – jbowman Nov 20 '13 at 0:09
• "Forget" statistics -this is a maximization problem. It can be solved also as a maximization problem under a non-negativity constraint on the variable, including additively the restriction in the objective function (the log-likelihood) with the associated KKT non-negative multiplier -and see what happens. – Alecos Papadopoulos Nov 20 '13 at 2:13
• @Alecos is correct: but the situation is simpler than that, because this is a one-variable problem. All you have to do is check whether the zero of the derivative is in the interval $[0,\infty)$. If not, the convexity of $-\log(L)$ guarantees that the boundary point $\theta=0$ is the maximum. Consult any elementary Calculus text for details. – whuber Nov 20 '13 at 3:15

Although the OP did not respond, I am answering this to showcase the method I proposed (and indicate what statistical intuition it may contain).

First, it is important to distinguish on which entity is the constraint imposed. In a deterministic optimization setting, there is no such issue : there is no "true value", and an estimator of it. We just have to find the optimizer. But in a stochastic setting, there are conceivably two different cases:

a) "Estimate the parameter given a sample that has been generated by a population that has a non-negative mean" (i.e. $\theta \ge 0$) and
b) "Estimate the parameter under the constraint that your estimator cannot take negative values"(i.e. $\hat \theta \ge 0$).

In the first case, imposing the constraint is including prior knowledge on the unknown parameter. In the second case, the constraint can be seen as reflecting a prior belief on the unknown parameter (or some technical, or "strategic", limitation of the estimator).

The mechanics of the solution are the same, though:The objective function, (the log-likelihood augmented by the non-negativity constraint on $\theta$) is

$$\tilde L(\theta|\mathbf{x})=-\frac n2 \ln(2\pi)-\frac{1}{2}\sum_{i=1}^{n}(x_i-\theta)^2 +\xi\theta,\qquad \xi\ge 0$$

Given concavity, f.o.c is also sufficient for a global maximum. We have

$$\frac {\partial}{\partial \theta}\tilde L(\theta|\mathbf{x})=\sum_{i=1}^{n}(x_i-\theta) +\xi = 0 \Rightarrow \hat \theta = \bar x+\frac{\xi}{n}$$

1) If the solution lies in an interior point ($\Rightarrow \hat \theta >0$), then $\xi=0$ and so the solution is $\{\hat \theta= \bar x>0,\; \xi^*=0\}$.

2) If the solution lies on the boundary ($\Rightarrow \hat \theta =0$) then we obtain the value of the multiplier at the solution $\xi^* = -n\hat x$, and so the full solution is $\{\hat \theta= 0,\; \xi^*=-n\bar x\}$. But since the multiplier must be non-negative, this necessarily implies that in this case we would have $\bar x\le 0$

(There is nothing special about setting the constraint to zero. If say the constraint was $\theta \ge -2$, then if the solution lied on the boundary, $\hat \theta = -2$, it would imply (in order for the multiplier to have a positive value), that $\bar x \le -2$).

So, if the optimizer is $0$ what are we facing here? If we are in "constraint type-a", i.e we have been told that the sample comes from a population that it has a non-negative mean, then with $\hat \theta =0$ chances are that the sample may not be representative of this population.

If we are in "constraint type-b", i.e. we had the belief that the population has a non-negative mean, with $\hat \theta =0$ this belief is questioned.

(This is essentially an alternative way to deal with prior beliefs, outside the formal bayesian approach).

Regarding the properties of the estimator, one should carefully distinguish this constrained estimation case, with the case where the true parameter lies on the boundary of the parameter space.

• Finally, is it correct to say that $\hat{\theta}_{\text{MLE}}=\max(\bar{X},0)$ ? – StubbornAtom May 2 '18 at 21:14
• @StubbornAtom This is a compact way indeed to express the situation prior to observe the sample and the sample mean. – Alecos Papadopoulos May 2 '18 at 21:22
• Another query: If $\theta$ does not take the value $0$ (i.e. if the parameter space is $(0,\infty)$), then would the MLE be simply $\hat{\theta}=\bar{X}$ ? – StubbornAtom May 2 '18 at 21:33
• @StubbornAtom Yes. Except if the sample proves us wrong and gives us a negative sample mean. – Alecos Papadopoulos May 2 '18 at 21:39
• And if the sample mean does give a negative value, what would be my MLE? – StubbornAtom May 2 '18 at 22:22