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DTSTART:20200101T000000
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DTSTART;VALUE=DATE:20200525
DTEND;VALUE=DATE:20200530
DTSTAMP:20200219T005529
CREATED:20200102T120029Z
LAST-MODIFIED:20200214T073823Z
UID:3290-1590364800-1590796799@www.eurandom.tue.nl
SUMMARY:Safe\, Anytime-Valid Inference (SAVI)
DESCRIPTION:Summary\nA large fraction of published research in top journals in applied sciences such as medicine and psychology has been claimed as irreproducable. In light of this 'replicability crisis'\, traditional methods for hypothesis testing\, most notably those based on p-values\, have come under intense scrutiny. One central problem is the following: if our test result is promising but non-conclusive (say\, p = 0.07) we cannot simply decide to gather a few more data points. While this practice is ubiquitous in science\, it invalidates p-values and error guarantees and makes the results of standard meta-analyses very hard to interpret. This issue is not unique for p-values: other approaches\, such as replacing testing by estimation with confidence intervals\, suffer from similar optional continuation problems. Over the last few years several distinct but closely related solutions have been proposed\, such as anytime-valid confidence sequences\, anytime-valid p-values\, and safe tests. \nRemarkably\, all these approaches can be understood in terms of (sequential) gambling. One formulates a gambling strategy under which one would not expect to gain any money if the null hypothesis were true. If for the given data one would have won a large amount of money in this game\, this provides evidence against the null hypothesis. The test statistic in traditional statistics gets replaced by the gambling strategy; the p-value gets replaced by the (virtual) amount of money gained. In more mathematical terms\, evidence against the null and confidence sets are derived in terms of non-negative supermartingales. While this idea in essence goes back to Wald’s sequential testing of the 1950's and its extensions by Robbins and co in the early 1960's and Lai in the 1970's\, it never really caught on because it used to be applicable only to very simple statistical models and testing scenarios. \nHowever\, recent work shows that this idea is essentially universally applicable – one can design supermartingales for large classes of tests\, both parametric and non-parametric\, and many estimation problems\, yielding anytime P-values using non-asymptotic versions of the law of the iterated logarithm. A variation of the idea\, the S-value\, can even be applied to completely arbitrary tests - at the price of only allowing for a weaker form of optional stopping. All these techniques have both a gambling and a Bayes factor interpretation. Thus\, these directions are able to somewhat unite Bayesian and frequentest ways of thinking; with the explicit ability to use prior knowledge\, with frequentest error control and confidence bounds\, but often using Bayesian techniques. \nThis workshop aims to get together two groups of researchers --- those who have been developing the mathematical\, probabilistic and statistical foundations of this area\, and practitioners who have studied and written about the reproducibility crisis in the sciences. \nThis conference supports the Welcoming Environment Statement of the Association for Women in Mathematics (AWM). \nSponsors\n \n \n \nOrganisers\n\n\n\nPeter Grünwald\nCWI\, Amsterdam\n\n\n\nAaditya Ramdas\nCarnegie Mellon University\n\n\n\n\n\n\n\n\n\n \nSpeakers\n\n\n\nAkshay Balsubramani\nStanford University\nGenetics\, Computer Science\n\n\nLeonhard Held\nUniversity of Zurich\nBiostatistics\, Reproducibility\n\n\nChris Jennison\nUniversity of Bath\nStatistics\n\n\nWouter Koolen\nCWI\, Amsterdam\nComputer Science (ML)\n\n\nDaniel Lakens\nTU Eindhoven\nHuman-Technology Interaction\, Reproducibility\n\n\nTheis Lange\nUniversity of Copenhagen\nBiostatistics\, Reproducibility\n\n\nAlexander Ly\nUniversity of Amsterdam and CWI\nPsychology\, Computer Science\n\n\nAlan Malek\nOptimizely\nStatistics\n\n\nDeborah Mayo\nVirginia Tech\nPhilosophy\n\n\nLuigi Pace\nUniversity of Udine\nEconomics\, Statistics\n\n\nLeonid Pekelis\nOpendoor\nStatistics\n\n\nDon van Ravenzwaaij\nGroningen University\n Psychologym Statistics\n\n\nChristian Robert\nUniversité Paris-Dauphine + Warwick University\nStatistics\n\n\nAlessandra Salvan\nUniversity of Padova\nStatistics\n\n\nGlenn Shafer\nRutgers University\nFoundations of Probability and Statistics\n\n\nMark Simmonds\nUniversity of York\nReview and Dissemination\n\n\nAnne Lyngholm Sørensen\nUniversity of Copenhagen\n Statistics\n\n\nVladimir Vovk\nRoyal Holloway London\nComputer Science (ML)\, Statistics\n\n\nEric-Jan Wagenmakers\nUniversity of Amsterdam\nPsychology\n\n\nRuodu Wang\nUniversity of Waterloo\nStatistics\, Actuarial Science\n\n\nLarry Wasserman\nCarnegie Mellon University\nStatistics\, ML\n\n\n\nProgramme\nWill follow soon \nAbstracts\nWill follow soon \nRegistration\nLink to online registration form: REGISTRATION\nRegistration is free\, but compulsory for all speakers and participants. \nPlease note: A possible cancellation of your participation needs to be done by email (koorn@eurandom.tue.nl) before Monday 18 May. When you don’t cancel your participation and you do not show up at the event\, we are unfortunately obliged to charge you 100 euro no-show fee (invoice will be sent to you). \n\nPractical Information\nLink to travel and accommodation information \n
URL:https://www.eurandom.tue.nl/event/safe-anytime-valid-inference-savi/
LOCATION:MF 11-12 (4th floor MetaForum Building\, TU/e)
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