I have the following sorted data (sampling from parametric space [1,5]) with respect to their distances of parameter Theta. i.e., Let say N = 1000,
Theta : 1.1, 1.7, 1.9, 2.4, 2.8, . . . , 4.9
Distance : 0.2, 0.3, 0.5, 0.9, 1.1, . . . , 1.9
From literature I know that more than 3% (i.e., 0.03*N) data are not useful, but I don't know where to cutoff? Could you suggest any re-sampling methods? How can I treat this problem? Classification or Regression?
This data is basically output of rejection sampling (see http://en.wikipedia.org/wiki/Approximate_Bayesian_computation). Now I hope that would be clear.