Nonparametric Statistics
Kernel methods, splines, and minimax rates for function estimation.
Nonparametric Statistics. Kernel methods, splines, and minimax rates for function estimation.
Foundations and canonical references
The standard treatments of nonparametric statistics approach the subject from complementary angles. Tsybakov, Introduction to Nonparametric Estimation (2009) is the anchor reference for the subject and lays out the core definitions, theorems, and worked examples that practitioners return to. Wasserman, All of Nonparametric Statistics (2006) gives a parallel, more proof-oriented exposition of the same material and is widely used as a graduate text.
Open methodological questions for nonparametric statistics include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- textbook · primary · 2009Introduction to Nonparametric Estimationtsybakov-2009
- textbook · primary · 2006All of Nonparametric Statisticswasserman-2006
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