Abstract
Like martingales, empirical processes also unify an incredibly large variety of problems in probability and statistics. Results in empirical processes theory are applicable to numerous classic and modern problems in probability and statistics; a few examples of applications are the study of central limit theorems in more general spaces than Euclidean spaces, the bootstrap, goodness of fit, density estimation, and machine learning. Familiarity with the basic theory of empirical processes is extremely useful across fields in probability and statistics.
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DasGupta, A. (2011). Empirical Processes and VC Theory. In: Probability for Statistics and Machine Learning. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9634-3_16
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DOI: https://doi.org/10.1007/978-1-4419-9634-3_16
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