## Tuesday, 21 July 2015

### Parametric Inference: Karlin-Rubin Theorem

A family of pdfs or pmfs $\{g(t|\theta):\theta\in\Theta\}$ for a univariate random variable $T$ with real-valued parameter $\theta$ has a monotone likelihood ratio (MLR) if, for every $\theta_2>\theta_1$, $g(t|\theta_2)/g(t|\theta_1)$ is a monotone (nonincreasing or nondecreasing) function of $t$ on $\{t:g(t|\theta_1)>0\;\text{or}\;g(t|\theta_2)>0\}$. Note that $c/0$ is defined as $\infty$ if $0< c$.
Consider testing $H_0:\theta\leq \theta_0$ versus $H_1:\theta>\theta_0$. Suppose that $T$ is a sufficient statistic for $\theta$ and the family of pdfs or pmfs $\{g(t|\theta):\theta\in\Theta\}$ of $T$ has an MLR. Then for any $t_0$, the test that rejects $H_0$ if and only if $T >t_0$ is a UMP level $\alpha$ test, where $\alpha=P_{\theta_0}(T >t_0)$.
Example 1
To better understand the theorem, consider a single observation, $X$, from $\mathrm{n}(\theta,1)$, and test the following hypotheses: $$H_0:\theta\leq \theta_0\quad\mathrm{versus}\quad H_1:\theta>\theta_0.$$ Then $\theta_1>\theta_0$, and the likelihood ratio test statistics would be $$\lambda(x)=\frac{f(x|\theta_1)}{f(x|\theta_0)}.$$ And we say that the null hypothesis is rejected if $\lambda(x)>k$. To see if the distribution of the sample has MLR property, we simplify the above equation as follows:

## Saturday, 23 May 2015

### Parametric Inference: Likelihood Ratio Test Problem 2

More on Likelihood Ratio Test, the following problem is originally from Casella and Berger (2001), exercise 8.12.

### Problem

For samples of size $n=1,4,16,64,100$ from a normal population with mean $\mu$ and known variance $\sigma^2$, plot the power function of the following LRTs (Likelihood Ratio Tests). Take $\alpha = .05$.
1. $H_0:\mu\leq 0$ versus $H_1:\mu>0$
2. $H_0:\mu=0$ versus $H_1:\mu\neq 0$

### Solution

1. The LRT statistic is given by $$\lambda(\mathbf{x})=\frac{\displaystyle\sup_{\mu\leq 0}\mathcal{L}(\mu|\mathbf{x})}{\displaystyle\sup_{-\infty<\mu<\infty}\mathcal{L}(\mu|\mathbf{x})}, \;\text{since }\sigma^2\text{ is known}.$$ The denominator can be expanded as follows: \begin{aligned} \sup_{-\infty<\mu<\infty}\mathcal{L}(\mu|\mathbf{x})&=\sup_{-\infty<\mu<\infty}\prod_{i=1}^{n}\frac{1}{\sqrt{2\pi}\sigma}\exp\left[-\frac{(x_i-\mu)^2}{2\sigma^2}\right]\\ &=\sup_{-\infty<\mu<\infty}\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-\mu)^2}{2\sigma^2}\right]\\ &=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-\bar{x})^2}{2\sigma^2}\right],\\ &\quad\text{since }\bar{x}\text{ is the MLE of }\mu.\\ &=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\frac{n-1}{n-1}\displaystyle\sum_{i=1}^{n}\frac{(x_i-\bar{x})^2}{2\sigma^2}\right]\\ &=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\frac{(n-1)s^2}{2\sigma^2}\right],\\ \end{aligned}

## Thursday, 21 May 2015

### Parametric Inference: Likelihood Ratio Test Problem 1

Another post for mathematical statistics, the problem below is originally from Casella and Berger (2001) (see Reference 1), exercise 8.6.

### Problem

1. Suppose that we have two independent random samples $X_1,\cdots, X_n$ are exponential($\theta$), and $Y_1,\cdots, Y_m$ are exponential($\mu$).
1. Find the LRT (Likelihood Ratio Test) of $H_0:\theta=\mu$ versus $H_1:\theta\neq\mu$.
2. Show that the test in part (a) can be based on the statistic
3. $$T=\frac{\sum X_i}{\sum X_i+\sum Y_i}.$$
4. Find the distribution of $T$ when $H_0$ is true.

### Solution

1. The Likelihood Ratio Test is given by $$\lambda(\mathbf{x},\mathbf{y}) = \frac{\displaystyle\sup_{\theta = \mu,\mu>0}\mathrm{P}(\mathbf{x},\mathbf{y}|\theta,\mu)}{\displaystyle\sup_{\theta > 0,\mu>0}\mathrm{P}(\mathbf{x}, \mathbf{y}|\theta,\mu)},$$ where the denominator is evaluated as follows: $$\sup_{\theta > 0,\mu>0}\mathrm{P}(\mathbf{x}, \mathbf{y}|\theta,\mu)= \sup_{\theta > 0}\mathrm{P}(\mathbf{x}|\theta)\sup_{\mu > 0}\mathrm{P}(\mathbf{y}|\mu),\quad\text{by independence.}$$ So that,