BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Julia Jansson (Chalmers University of Technology & University of G othenburg) DTSTART:20250109T083000Z DTEND:20250109T103000Z DTSTAMP:20260422T161048Z UID:gbgstats/77 DESCRIPTION:Title: Licentiate seminar: Statistical Properties of Point Process Learning for Gibbs Processes\nby Julia Jansson (Chalmers University of Technology & University of Gothenburg) as part of Gothenburg statistics seminar\n\nLe cture held in Pascal\, Hörsalsvägen 1.\n\nAbstract\nThis thesis studies Point Process Learning (PPL)\, which is a novel statistical learning frame work that uses point process cross-validation and point process prediction errors\, and includes different hyperparameters. Specifically\, statistic al properties of PPL are explored\, in the context of Gibbs point processe s. Paper 1 demonstrates PPL’s advantages over pseudolikelihood\, which i s a state-of-the-art parameter estimation method and a special case of Tak acs- Fiksel estimation (TF)\, with particular focus on Gibbs hard-core pro cesses. Paper 2 compares PPL to TF\, and shows that TF is a special case o f PPL\, when the cross-validation scheme tends to leave-one-out cross-vali dation. In addition\, Paper 2 shows that for four common Gibbs models\, na mely Poisson\, hard-core\, Strauss and Geyer saturation processes\, one ca n choose hyperparameters so that PPL outperforms TF in terms of mean squar e error.\n\nIn Paper 1 and 2\, parameter estimation with PPL is done by mi nimizing loss functions\, while Paper 3 explores an alternative approach t o PPL\, namely estimating equations. Further\, statistical properties of t he parameter estimator are derived in Paper 3\, such as consistency and as ymptotic normality for large samples\, as well as bias and variance for sm all samples. It is concluded that the estimating equation approach is not feasible for PPL\, whereby the original loss function-based approach is pr eferred. Moving on\, Paper 3 then provides a theoretical foundation for th e loss functions through an empirical risk formulation.\n\nTo conclude\, P PL is shown to be a flexible and robust competitor to state-of-the-art met hods for parameter estimation.\n\nRoom: Pascal\, Hörsalsvägen 1\n LOCATION:https://researchseminars.org/talk/gbgstats/77/ END:VEVENT END:VCALENDAR