All of Statistics: a Concise Course in Statistical Inference

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Kernel-Based Methods for Hypothesis Testing: A Unified View (2013, methodology): “x x x x , , , , i n i n 1 1 2 2 f f This can be summarized by the following diagram, paraphrased from [31]:”
Identifying functional modules in protein–protein interaction networks: an integrated exact approach (2008, methodology): “Because p-values are uniformly distributed under the null hypothesis (Wasserman, 2005), we apply Eq.”
Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals (2013, background): “In practice, interval estimates must come with a confidence guarantee: if we say an interval is an (1 − α)% CI, we should be assured that it covers the true value in at least (1 − α)% of datasets [16,17,18].”
Support Vector Machines Used to Estimate the Battery State of Charge (2013, methodology): “The coefﬁcient of determination was chosen in this research study to estimate the goodness of ﬁt of the SVM model [27].”
A Bootstrap Based Neyman-Pearson Test for Identifying Variable Importance (2015, background): “Theorem 3.1 (Hoeffding’s Inequality [10]): and a ≤ Y i ≤ b .”
Power-Law Distributions in Empirical Data (2007, methodology): “The method of choice for fitting parametrized models such as power-law distributions to observed data is the method of maximum likelihood, which provably gives accurate (asymptotically normal) parameter estimates in the limit of large sample size (Barndorff-Nielsen and Cox, 1995; Wasserman, 2003).”