I need to obtain the functional (i.e.: statistic obtained from the empirical distribution function) associated to the most powerful test to contrast $H_0: f_o$ vs. $H_1: f_1$, using a sample of size $n$.
In particular, my problem is that I don’t know how to construct this functional when I am not given a particular density function.
Using the Neyman Pearson lemma, I know that the maximum power test can be performed by computing the likelihood ratio test as:
$$ \Lambda(x_1,...,x_n)=\dfrac{{\sup}f_0(x_1,...,x_n)} {{\sup}f_1(x_1,...,x_n)} $$
However since I don’t have a specific density function, I don’t know if this expression can be further operated or simplified in any way.
EDIT: Since I got no answers or comments, my guess is that there may be something wrong with my question.
EDIT 2: Per comments I understand now that I should use non parametric statistics and instead of conventional maximum likelihood, the appropriate ratio to use should be based on empirical likelihood.
In the introduction of this paper: Empirical Likelihood Based Hypothesis Testing, there is a derivation of an statistic based on Empirical likelihood ratio to test $H_0: F = F_0$. However, since my data do not have the prerequisite of $F$ being a continuous distribution I am not sure if such reasoning can be applied to my original question.