Nonparametric Statistics for SPDEs

Add to your list(s) Download to your calendar using vCal

• Markus Reiss (Humboldt University of Berlin)
• Friday 10 November 2023, 14:00-15:00
• MR12, Centre for Mathematical Sciences.

If you have a question about this talk, please contact Qingyuan Zhao.

Stochastic partial differential equations (SPDEs) are used more and more often to model real-world phenomena. Currently, statistical methodology for these equations driven by space-time white noise is developing rapidly. Based on the classical spectral method for parametric drift estimation, we shall exhibit fundamental differences with the case of stochastic ordinary differential equations. This method, however, is restricted to simple parametric situations and we discuss the local estimation method in detail, which allows to estimate varying coefficients in the differential operator of a parabolic SPDE nonparametrically with optimal rates. This approach is extended to observations under measurement errors (‘static noise’), showing a fundamentally different impact of dynamic and static noise levels. Finally, we present an abstract minimax lower bound framework for stochastic evolution equations generated by normal operators in Hilbert space and obtain a rich picture of complexity for different SPDE estimation objectives. Some illustrations with cell motility experiments in biophysics are provided.

This talk is part of the Statistics series.

This talk is included in these lists:

• All CMS events
• All Talks (aka the CURE list)
• CMS Events
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge talks
• Chris Davis' list
• DPMMS Lists
• DPMMS info aggregator
• DPMMS lists
• Guy Emerson's list
• Hanchen DaDaDash
• Interested Talks
• MR12, Centre for Mathematical Sciences
• Machine Learning
• School of Physical Sciences
• Statistical Laboratory info aggregator
• Statistics
• Statistics Group
• bld31
• custom
• rp587

Note that ex-directory lists are not shown.