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DTSTART;TZID=Europe/Helsinki:20241209T140000
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SUMMARY:Pragya SUR (Harvard University​) - Spectrum-Aware Debiasing: A Modern Inference Framework with Applications to Principal Components Regression
DESCRIPTION:Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:00 pm\nDate: 9th December\nPlace: 3001 \n  \nPragya SUR (Harvard University​) – Spectrum-Aware Debiasing: A Modern Inference Framework with Applications to Principal Components Regression \n  \n Abstract:  \nDebiasing methodologies have emerged as powerful tools for making statistical inferences in high-dimensional problems. Since its original introduction\, the methodology underwent a major development with the introduction of debiasing techniques that adjust for degrees-of-freedom (Bellec and Zhang\, 2019). While overcoming limitations of initial debiasing approaches\, this updated method relies on Gaussian/sub-Gaussian tailed designs and independent\, identically distributed samples – a key limitation. In this talk\, I will propose a novel debiasing formula that breaks this barrier by exploiting the spectrum of the sample covariance matrix. Our formula applies to a much broader class of designs\, including some heavy- tailed distributions\, as well as certain dependent data settings. Our correction term differs significantly from prior work but recovers the Gaussian-based formula as a special case. Notably\, our approach does not require estimating the high-dimensional population covariance matrix yet can account for certain classes of dependence among both features and samples. We demonstrate the utility of our method for several statistical inference problems. As a by-product\, our work also introduces the first debiased principal component regression estimator with formal guarantees in high dimensions. \n  \nOrganizers: \nAnna KORBA (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST) \nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/pragya-sur-spectrum-aware-debiasing-a-modern-inference-framework-with-applications-to-principal-components-regression/
CATEGORIES:Seminars,Statistics
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