BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CREST - ECPv5.1.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:CREST
X-ORIGINAL-URL:https://crest.science
X-WR-CALDESC:Events for CREST
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20200329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20201025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20201116T140000
DTEND;TZID=Europe/Paris:20201116T151500
DTSTAMP:20240223T173706
CREATED:20201016T081739Z
LAST-MODIFIED:20201016T081739Z
UID:12480-1605535200-1605539700@crest.science
SUMMARY:Aaditya RAMDAS (Carnegie Mellon University) - " Dimension-agnostic inference "
DESCRIPTION:\nThe Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 16th of November 2020\nPlace: Visio\nAaditya RAMDAS (Carnegie Mellon University) – ” Dimension-agnostic inference “ \nAbstract: Classical asymptotic theory for statistical hypothesis testing\, for example Wilks’ theorem for likelihood ratios\, usually involves calibrating the test statistic by fixing the dimension d while letting the sample size n increase to infinity. In the last few decades\, a great deal of effort has been dedicated towards understanding how these methods behave in high-dimensional settings\, where d_n and n both increase to infinity together at some prescribed relative rate. This often leads to different tests in the two settings\, depending on the assumptions about the dimensionality. This leaves the practitioner in a bind: given a dataset with 100 samples in 20 dimensions\, should they calibrate by assuming n >> d\, or d_n/n \approx 0.2? This talk considers the goal of dimension-agnostic inference—developing methods whose validity does not depend on any assumption on d_n. I will summarize results from 3 papers that achieve this goal in parametric and nonparametric settings. \n(A) Universal inference (PNAS’20) https://arxiv.org/abs/1912.11436 — we describe the split/crossfit likelihood ratio test whose validity holds nonasymptotically without any regularity conditions.\n(B) Classification accuracy as a proxy for two-sample testing (Annals of Statistics’21) https://arxiv.org/abs/1602.02210.\n(C) Dimension-agnostic inference (http://arxiv.org/abs/2011.05068)— We describe a generic approach that uses variational representations of existing test statistics along with sample splitting and self-normalization (studentization) to produce a Gaussian limiting null distribution. We exemplify this technique for a handful of classical problems\, such as one-sample mean testing\, testing if a covariance matrix equals the identity\, and kernel methods for testing equality of distributions using degenerate U-statistics like the maximum mean discrepancy. Without explicitly targeting the high-dimensional setting\, our tests are shown to be minimax rate-optimal\, meaning that the power of our tests cannot be improved further up to a constant factor of \sqrt{2}.\nThis is primarily joint work with several excellent coauthors including Ilmun Kim (postdoc\, Cambridge)\, Larry Wasserman\, Sivaraman Balakrishnan and Aarti Singh.\nAaditya Ramdas (http://www.stat.cmu.edu/~aramdas/)\nOrganizers:\nCristina BUTUCEA (CREST)\, Alexandre TSYBAKOV (CREST)\, Karim LOUNICI (CMAP) \, Zoltan SZABO (CMAP)\nSponsors:\nCREST-CMAP\n \n\n
URL:https://crest.science/event/aaditya-ramdas/
CATEGORIES:Statistics
ATTACH;FMTTYPE=:
END:VEVENT
END:VCALENDAR