Suppose "x_1,\\ldots,x_n" is a random sample from a population. We will find the test with the best critical region, that is, find the uniformly most powerful test, with a sample size "n" and a significance level "\\alpha" to test the simple "H_0: \\theta=\\theta_0" against the composite "H_1: \\theta>\\theta_0".
Let "\\alpha=P(C;\\theta_0)".
For each simple "H_1: \\theta=\\theta_a", say, the ratio of the likelihood functions is:
"\\frac{L(\\theta_0)}{L(\\theta_a)}=\\frac{\\frac{1+\\theta_0}{(x_1+2)^2}\\ldots\\frac{1+\\theta_0}{(x_n+2)^2}}{\\frac{1+\\theta_a}{(x_1+2)^2}\\ldots\\frac{1+\\theta_a}{(x_n+2)^2}}\\leq k, k=const.\\\\\n(\\frac{1+\\theta_0}{1+\\theta_a})^n\\leq k.\\\\\n\\frac{1+\\theta_0}{1+\\theta_a}\\leq k^{1\/n}."
Here we use the Neyman Pearson Lemma.
The left side of the last inequality does not depend on "x_1,\\ldots,x_n". So each critical region is "the best". The best critical region does not exist.
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