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By Yen-Chi Chen, explicitly named on the lecture page. Authored notes in UW STAT 535: Statistical Machine Learning, Autumn 2026, hosted on the University of Washington faculty website. No separate lecture publication or revision date is established.

Online text: faculty.washington.edu/yenchic/25A_stat535/Lec12_bootstrap.html

Cited passages: The Bootstrap, resampling procedure; High level idea of how bootstrap works, conditional empirical sampling law; Bootstrap and Statistical Functionals, conditional variance and consistency; Failure of the bootstrap, uniform-endpoint counterexample.

Yen-Chi Chen works on nonparametric statistics, Causal inference, missing data and geometric analysis of data. His University of Washington research page also lists mixture models, astrostatistics and applied statistics.^[ref]

His bootstrap notes explain the distinction between the unknown population distribution and the empirical distribution used for resampling. They also discuss when a bootstrap approximation can fail, including an endpoint of a uniform distribution. This is a useful bridge between a computational recipe and the mathematical conditions needed for reliable statistical inference.^[ref]

Homepage: faculty.washington.edu/yenchic/