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    Gaussian kernel density weight question (in Python)

    Never mind ... figured it out. It's simple normalization of Gaussian function of (x_i - x) where x is restricted to values between 0 and 1.
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    Gaussian kernel density weight question (in Python)

    Hi, I'm trying to decipher some code ... class Gaussian(BaseKernel): def _compute_weights(self): if not self.fix_boundary: return(1.) weights = np.zeros(self.data.shape[0]) for i,d in enumerate(self.data): weights =...
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    Combining Benjamini-Hochberg with statistical power calculations

    It's not a question of publication - everyday procedure. What is "official"/best practice?
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    Combining Benjamini-Hochberg with statistical power calculations

    Some of them have low power because the tests are unconditioned. So you would say, perform all the tests, filter according to the BH procedure, then throw out tests that turn out not to be sufficiently powerful at the end of the pipeline?
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    Combining Benjamini-Hochberg with statistical power calculations

    Suppose I am investigating a question which involves e.g., many statistical T-tests. The normal Benjamini-Hochberg procedures tells me how to control the false discovery rate. However, suppose that some or many of these tests do not have sufficient statistical power i.e., it falls below 0.8...