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Consider a random sample of size $n$ from a distribution with probability density function (pdf) is given by

$$f(x;\theta)=\left\{\begin{matrix} \sqrt{\frac{2}{\pi}}e^{-\frac{1}{2}(x-\theta)^2}&\text{if }\space x\geq \theta \\ 0&\text{elsewhere} \end{matrix}\right.$$

Find the maximum likelihood estimator of $\theta$.

My attempt,

$$L(\theta)=\prod_{i=1}^{n}\sqrt{\frac{2}{\pi}}e^{-1/2(x_i-\theta)^2}$$

$$=\exp(-\frac{1}{2}\sum_{i=1}^{n}(x_i-\theta)^2)\cdot constant$$

$$\log L=-\frac{1}{2}\sum_{i=1}^{n}(x_i-\theta)^2+constant$$

$$\frac{\delta}{\delta \theta}\log L=\sum_{i=1}^{n}(x_i-\theta)$$

$$=\sum_{i=1}^{n}x_i-n\theta$$

$$n\theta=\sum_{i=1}^{n}x_i$$

$$\hat{\theta}=\bar{x}$$

Am I correct?

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    $\begingroup$ Yes you found how to get the MLE for the mean of a Gaussian distributed sample. $\endgroup$ – Sextus Empiricus Jun 19 '18 at 14:53
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    $\begingroup$ @MartijnWeterings Given the constraint on the support, which leaves us with the right half of a unit variance Gaussian, I would imagine the MLE of $\theta$ is much smaller than $\bar{x}$ $\endgroup$ – khol Jun 19 '18 at 15:30
  • $\begingroup$ When the support depends on the parameter, instead of flatly differentiating (which isn't really justified here), pay attention to the parameter space given the sample. $\endgroup$ – StubbornAtom Jun 19 '18 at 15:50
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    $\begingroup$ @khol I had missed that part. It is indeed as Jarle stated. For the half normal distribution the likelihood function is zero unless $\theta$ is smaller than the smallest $x_i$. Thus you get a $\mathcal{L}(\theta)$ which is a parabolic function centered at $\bar{x}>x_{(1)}$, but cut off to 0 at the point $x_{(1)}$. $\endgroup$ – Sextus Empiricus Jun 19 '18 at 16:59
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For $\theta\le x_{(1)}$ the likelihood is an increasing function of $\theta$ and for $\theta>x_{(1)}$, the likelihood is zero. Hence, the MLE of $\theta$ is $\hat\theta=X_{(1)}$.

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