Past exam of the mathematics course of the University of Cambridge / 2007 / ib / Paper 2 / 19C (Statistics)

The Neyman-Pearson lemma concerns a simple hypothesis with density $f_0$ against a simple alternative with density $f_1$. Allow a randomized test $0\le \phi \le 1$. Choose a threshold $c\ge 0$ and, if necessary, randomize on the equality set so thatThen ${\phi }_{\ast }$ is a most powerful test among all tests of size at most $\alpha$. To prove it, let $E_0\phi \le \alpha$. Pointwise, $({\phi }_{\ast }-\phi )(f_1-c{f}_0)\ge 0$: above threshold both factors are nonnegative; below threshold both are nonpositive; on the threshold their product vanishes. ThereforeThis proves the lemma, including regions with $f_0=0$, where a positive alternative density must be rejected. It also shows that ties in the likelihood ratio are the only discretionary region when the inequality is strict off the threshold.

For the stated family, $R=|X|$ has the Gamma distribution with shape $\theta$ and unit rate. At $\theta =1$ it has the exponential distribution, and the likelihood ratio for $\theta =2$ against $\theta =1$ isbecause $\Gamma (1)=\Gamma (2)=1$. The most powerful test therefore rejects for large $|X|$. Since ${\mathrm{Pr}}_1(|X|>c)=e^{-c}$, its exact size-$\alpha$ critical value and rejection rule areThe distribution is continuous, so boundary randomization is unnecessary. Under $\theta =2$, integration by parts gives the power function of a statistical testFor every fixed $\theta >1$, the likelihood ratio against the same null is $|x{|}^{\theta -1}/\Gamma (\theta )$, strictly increasing in $|x|$. The size constraint still selects exactly the same critical value $c=-\log \alpha$, independent of the alternative. The Neyman-Pearson lemma therefore makes this single test most powerful against each $\theta >1$ separately. Hence it is uniformly most powerful for the composite alternative $\theta >1$.