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Everything about Distributional data analysis

Branch of nonparametric statistics

Distributional data analysis is a branch of nonparametric statistics that is related to functional data analysis. It is concerned with random objects that are probability distributions, i.e., the statistical analysis of samples of random distributions where each atom of a sample is a distribution. One of the main challenges in distributional data analysis is that although the space of probability distributions is a convex space, it is not a vector space. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Distributional data analysisA 5-minute orientation
  2. Skim a recent paper: Physical activity phenotypes and mortality in older adults: a novel distributional data analysis of accelerometry in the NHANESInfluential work since 2022
  3. Pick up a classic book: Internet and Distributed Computing SystemsThe most-reprinted book on the subject
  4. Read the landmark paper: Glucodensities: A new representation of glucose profiles using distributional data analysisCited 48 times
  5. Go sideways: Wasserstein metricA closely related topic
  6. Go sideways: McKean–Vlasov processA closely related topic

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🏆 Landmark papers (most cited)

🆕 Influential since 2022

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