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DTSTART;TZID=UTC:20240506T130000
DTEND;TZID=UTC:20240523T161500
DTSTAMP:20260710T184959
CREATED:20231019T102955Z
LAST-MODIFIED:20240326T092120Z
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SUMMARY:Estimation of Functionals of High-Dimensional Parameters: Bias Reduction and Concentration\, Vladimir Koltchinskii (Georgia Institute of Technology)
DESCRIPTION:  \n\n\n\n  \n  \nSCHEDULE\n  \nMonday\n  \n6th May 2024 \n13th May 2024\n  \nFrom 13:00 to 16:15\n  \nRoom 2033\n\n\n  \nThursday\n  \n16th May 2024 \n23rd May 2024\n  \nFrom 13:00 to 16:15\n  \nRoom 2033\n\n\n\nAims and objectives\nThe aim of this course will be on a circle of problems related to estimation of real valued functionals of parameters of high-dimensional statistical models. In such problems\, it is of interest to estimate onedimensional features of a high-dimensional parameter that are often represented by nonlinear functionals of certain degree of smoothness defined on the parameter space. The functionals of interest could be estimated with faster convergence rates than the whole parameter (sometimes\, even with parametric rates). The examples include\, for instance\, such problems as estimation of linear functionals of principal components (that are nonlinear functionals of unknown covariance) in high-dimensional PCA. The goal is to discuss several mathematical methods that provide a way to develop estimators of functionals of highdimensional parameters with optimal error rates in classes of functionals of some Hölder smoothness.\nMoreover\, when the degree of smoothness of the functional is above certain threshold\, the estimators in question have parametric √𝑛 error rate and are asymptotically efficient\, whereas the error rates become slower than √𝑛 when the degree of smoothness is below the threshold.\nThe following topics will be covered (at least\, to some extent):\n• preliminaries in high-dimensional probability and analysis (concentration inequalities\, comparison inequalities\, Hölder smoothness of operator functions\, etc);\n• non-asymptotic bounds and concentration inequalities for sample covariance in high-dimensional and dimension-free frameworks;\n• some approaches to concentration inequalities for smooth functionals of statistical estimators;\n• higher order bias reduction methods in functional estimation;\n– methods based on Taylor expansion and estimation of polynomials with reduced bias;\n– iterative bias reduction and bootstrap chains;\n– linear aggregation of plug-in estimators with different sample sizes and jackknife estimators;\n• minimax lower bounds in functional estimation (applications of van Trees inequality\, Nemirovski’s construction of least favorable functionals\, etc);\n• Examples:\n– high-dimensional and infinite dimensional Gaussian models: functionals of mean and of covariance;\n– log-concave models\, in particular\, log-concave location families;\n– high-dimensional exponential families;\n– nonparametric models\, functionals of unknown density;\n– linear functionals of spectral projections of matrix parameters. \n\n
URL:https://crest.science/event/estimation-of-functionals-of-high-dimensional-parameters-bias-reduction-and-concentration-vladimir-koltchinskii-georgia-institute-of-technology/
LOCATION:2033
CATEGORIES:Doctoral Courses,Statistics
ORGANIZER;CN="Alexandre%20Tsybakov":MAILTO:alexandre.tsybakov@ensae.fr
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20240513
DTEND;VALUE=DATE:20240518
DTSTAMP:20260710T184959
CREATED:20240514T073119Z
LAST-MODIFIED:20240514T073119Z
UID:17019-1715558400-1715990399@crest.science
SUMMARY:6th Workshop on Sequential Monte Carlo Methods (SMC 2024)
DESCRIPTION:13 – 17 May 2024 \nWith the paprticipation of Nicolas Chopin \nhttps://www.icms.org.uk/SMC2024 \n
URL:https://crest.science/event/6th-workshop-on-sequential-monte-carlo-methods-smc-2024/
CATEGORIES:Conferences and Workshops,Statistics
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20240516T100000
DTEND;TZID=Europe/Helsinki:20240516T110000
DTSTAMP:20260710T184959
CREATED:20240214T083417Z
LAST-MODIFIED:20240424T085436Z
UID:16690-1715853600-1715857200@crest.science
SUMMARY:Jose OLMO (Univ. of Zarragoza and Univ. of Southampton.) "MEASURING AND TESTING SYSTEMIC RISK FROM THE CROSS-SECTION OF STOCK RETURNS"
DESCRIPTION:Finance & Financial Econometrics : \nTime: 10:00 am\nDate: 16th of May 2023\nRoom 3001 \nJose OLMO (Univ. of Zarragoza Univ. of Southampton) “MEASURING AND TESTING SYSTEMIC RISK FROM THE CROSS-SECTION OF STOCK RETURNS” \nAbstract : This study proposes a novel measure of systemic risk that is obtained by aggregating downside risk information from the cross section of assets. In contrast to existing studies\, we expand the analysis of systemic risk to many assets and focus on marginal measures of tail risk that are aggregated using a Fisher type test to detect the risk of systemic events. The presence of downside risk for each asset of the cross section is examined through a bootstrap test of first order stochastic dominance between the underlying tail distribution and the tail distribution of the residuals of a multivariate DCC-GARCH model. The application of these methods to the cross section of the FTSE-100 stock returns provides overwhelming evidence on the presence of financial instability during the period 2006-2009. Interestingly\, we also find compelling evidence of systemic risk during the 2012-2015 period coinciding with the European debt crisis and after the outbreak of the COVID-19 pandemic.\n(joint with Jesus Gil-Jaime)\n \nOrganizers:\n\nJean-Michel ZAKOIAN (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/jose-olmo-univ-of-zarragoza-t-b-a/
CATEGORIES:Finance-Insurance,Financial Econometrics,Seminars
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BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20240516T110000
DTEND;TZID=Europe/Helsinki:20240516T120000
DTSTAMP:20260710T184959
CREATED:20240214T083237Z
LAST-MODIFIED:20240424T085224Z
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SUMMARY:Stefan VOIGT (Univ. of Copenhagen) "MARKET RESPONSES TO A VIX IMPULSE"
DESCRIPTION:Finance & Financial Econometrics : \nTime: 11.00 am\nDate: 16th of May 2023\nRoom 3001 \nStefan VOIGT (Univ. of Copenhagen) “MARKET RESPONSES TO A VIX IMPULSE ” \nAbstract : Implied variance (VIX) impulses can be caused by either (i) an increase in expected future realized variance\, or (ii) an increase in the variance risk premium. We analyze twenty billion NASDAQ order book messages for equity and government-bond exchange-traded funds to delineate how the market responds to shocks in either of these two components. The response to a variance risk premium shock is that investors actively sell equities and buy government bonds on largely unchanged liquidity. The response to a expected realized variance shock\, on the other hand\, is active buying of equities on worse liquidity. We provide intuition for these findings.\n(joint with Nikolaus Hautsch and Albert J. Menkveld)\n \nOrganizers:\n\nJean-Michel ZAKOIAN (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/stefan-voigt-univ-of-copenhagen-t-b-a/
CATEGORIES:Finance-Insurance,Financial Econometrics,Seminars
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BEGIN:VEVENT
DTSTART;TZID=Europe/Helsinki:20240516T120000
DTEND;TZID=Europe/Helsinki:20240516T133000
DTSTAMP:20260710T184959
CREATED:20240326T155925Z
LAST-MODIFIED:20240326T155947Z
UID:16885-1715860800-1715866200@crest.science
SUMMARY:Gaël LE MENS (Universitat Pompeu Fabra) - Scaling Political Texts with ChatGPT
DESCRIPTION:Sociology seminar – Thursdays\nTime: 12:00 pm – 1:30 pm \nDate: 16th May 2024\nPlace: room 3105 \nZOOM LINK: https://zoom.us/j/97751150432?pwd=NUpGdDg1OW9uSFNxZWNxUTR0ZjRNUT09 \n\nGaël LE MENS (Universitat Pompeu Fabra) – Scaling Political Texts with ChatGPT\n  \nAbstract: \nWe use GPT-4 to obtain position estimates of political texts in continuous spaces. We develop and validate a new approach by positioning British party manifestos on the economic\, social\, and immigration policy dimensions and tweets by members of the US Congress on the left-right ideological spectrum. For the party manifestos\, the correlation between the positions produced by GPT-4 and experts is 93% or higher\, a performance similar to or better than that obtained with crowdsourced position estimates. For individual tweets\, the positions obtained with GPT-4 achieve a correlation of 91% with crowdsourced position estimates. For senators of the 117th US Congress\, the positions obtained with GPT-4 achieve a correlation of 97% with estimates based on roll call votes and of 96% with those based on campaign funding. Correlations are also substantial within party\, indicating that position estimates produced with GPT-4 capture within-party differences between senators. Overall\, using GPT-4 for ideological scaling is fast\, cost-efficient\, and reliable. This approach provides a viable alternative to scaling by both expert raters and crowdsourcing. \n  \nOrganizers: Annina Cleasson\, Paola Tubaro\, Patrick Präg (CREST Sociology unit) \n  \nSponsors: CREST \n  \n
URL:https://crest.science/event/gael-le-mens-universitat-pompeu-fabra-scaling-political-texts-with-chatgpt/
CATEGORIES:Seminars,Sociology
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