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Mikael Kuusela, SAMSI and UNC-Chapel Hill

January 19 @ 3:30 pm - 4:30 pm

Locally stationary spatio-temporal interpolation of Argo profiling float data   Argo floats measure sea water temperature and salinity in the upper 2,000 m of the global ocean. The statistical analysis of the resulting spatio-temporal data set is challenging due to its nonstationary structure and large size. I propose mapping these data using locally stationary Gaussian process regression where covariance parameter estimation and spatio-temporal prediction are carried out in a moving-window fashion. This yields computationally tractable nonstationary anomaly fields without the…

Jason Xu, UCLA

January 22 @ 3:30 pm - 4:30 pm

Enabling likelihood-based inference for complex and dependent data   The likelihood function is central to many statistical procedures, but poses challenges in classical and modern data settings. Motivated by emergent cell lineage tracking experiments to study blood cell production, we present recent methodology enabling likelihood-based inference for partially observed data arising from continuous-time stochastic processes with countable state space. These computational advances allow principled procedures such as maximum likelihood estimation, posterior inference, and expectation-maximization (EM) algorithms in previously intractable data…

Shizhe Chen

January 24 @ 3:30 pm - 4:30 pm

Robin Gong

January 26 @ 3:30 pm - 4:30 pm

Sara Algeri

January 29 @ 3:30 pm - 4:30 pm
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