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X-WR-CALDESC:Events for CREST
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DTSTART:20230326T010000
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DTSTART:20231029T010000
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DTSTART;TZID=Europe/Helsinki:20230511T030000
DTEND;TZID=Europe/Helsinki:20230511T163000
DTSTAMP:20260712T190404
CREATED:20230323T125541Z
LAST-MODIFIED:20230324T142055Z
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SUMMARY:Derek KREAGER - "Social Networks and Health in Prison and Upon Reentry: Lessons Learned from the Prison Inmate Network Studies (PINS)" ZOOM
DESCRIPTION:Sociology Seminar: Thursdays\nTime: 3:00 pm – 4:30 pm – \nDate: 11th of May 2023\nPlace: ZOOM \n  \nDerek KREAGER – “Social Networks and Health in Prison and Upon Reentry: Lessons Learned from the Prison Inmate Network Studies (PINS)”  \n  \nhttps://zoom.us/j/97668225972?pwd=Qm4wOG1vcmVNR3hqTTFVNllubVNIdz09   \n  \nAbstract :  \nThis presentation overviews the theory\, methods\, and results from a series of studies focused on the informal social networks and health of men and women incarcerated in Pennsylvania prisons. Through the collection of unique social network and qualitative data focused on friendship and status in multiple prison settings\, we test longstanding hypotheses for the sources of prison status and expected associations between individual social positions and outcomes such as prison victimization\, treatment engagement\, mental health\, and reentry success. I summarize the findings and challenges of this mode of research and its relevance for criminological theory and policy. Finally\, I introduce two ongoing research projects that extend this network approach into the domains of (a) corrections officer health and social organization and (b) a reentry house focused on long-term incarcerated men. \n  \n  \nHadrien Le Mer\, Etienne OLLION\, Patrick PRÄG (Pôle de Sociologie du CREST)\nSponsors :\nCREST \n
URL:https://crest.science/event/derek-kreager-social-networks-and-health-in-prison-and-upon-reentry-lessons-learned-from-the-prison-inmate-network-studies-pins-zoom/
CATEGORIES:Doctoral Courses,Sociology
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DTSTART;TZID=Europe/Helsinki:20230511T103000
DTEND;TZID=Europe/Helsinki:20230511T233000
DTSTAMP:20260712T190404
CREATED:20230425T063248Z
LAST-MODIFIED:20230428T112613Z
UID:14893-1683801000-1683847800@crest.science
SUMMARY:Dick VAN DIJK (Erasmus University Rotterdam) "Robust Observation-Driven Models Using Proximal-Parameter Updates"
DESCRIPTION:Finance & Financial Econometrics: \nTime: 10.30 am\nDate: 11th of May 2023\nRoom 3001 \nDick VAN DIJK (Erasmus University Rotterdam) “Robust Observation-Driven Models Using Proximal-Parameter Updates” \nAbstract : We propose an observation-driven modelling framework that permits time variation in the model’s parameters using a proximal-parameter (ProPar) update. ProPar maximizes the observation log-density with respect to the parameter vector\, while penalizing the weighted L2-norm relative to the one-step-ahead prediction. This yields an implicit stochastic-gradient update; taking instead the explicit version would produce the popular class of score-driven models. For log-concave observation densities (even when misspecified)\, ProPar’s robustness is evident from its muted response to outliers\, stability under poorly specified learning rates\, and global contractivity towards a pseudo-truth. We illustrate ProPar’s usefulness for estimating time-varying regressions\, volatility\, and quantiles. \nJean-Michel ZAKOIAN (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/dick-van-dijk-erasmus-university-rotterdam-t-b-a/
CATEGORIES:Finance-Insurance,Financial Econometrics
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DTSTART;TZID=Europe/Helsinki:20230511T113000
DTEND;TZID=Europe/Helsinki:20230511T123000
DTSTAMP:20260712T190404
CREATED:20230425T063433Z
LAST-MODIFIED:20230426T035835Z
UID:14894-1683804600-1683808200@crest.science
SUMMARY:Pierluigi VALLARINO (Aarhus University) "Time-Varying Kernel Densities as Dynamic Infinite Mixture Models"
DESCRIPTION:Finance & Financial Econometrics: \nTime: 11.30 am\nDate: 11th of May 2023\nRoom 3001 \nPierluigi VALLARINO (Aarhus University) “Time-Varying Kernel Densities as Dynamic Infinite Mixture Models” \nAbstract : Building on kernel density estimation for time series data\, we introduce the family of Dynamic Infinite Mixture Models (DIMMs). DIMMs approximate the time-varying distribution of a time series with that of an infinite mixture of location-scale random variables. Different specifications of a DIMM can capture different features of the time series of interest\, such as different memory properties of the predictive mean and asymmetric effects in the predictive variance. A maximum likelihood estimator is proposed. Its asymptotic properties are studied under a fully misspecified setting and its finite sample behaviour is assessed in a Monte Carlo analysis. An application to US GDP growth shows that DIMMs: i) improve upon extant kernel density approaches for time series data; ii) reliably track the time-varying distribution of interest; iii) perform on par with – if not better than – a fully-fledged parametric model when it comes to predicting probability density functions. \nOrganizers:\nJean-Michel ZAKOIAN (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/pierluigi-vallarino-aarhus-university-time-varying-kernel-densities-as-dynamic-infinite-mixture-models/
CATEGORIES:Finance-Insurance,Financial Econometrics
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