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Modeling and Inference of Local Stationarity

Prof Hsing Tailen Saw Swee Hock Professor of Statistics, NUS and Michael B. Woodroofe Collegiate Professor of Statistics, University of Michigan

Date:28 March 2018, Wednesday

Location:S16-06-118, DSAP Seminar Room

Time:02:00pm - 03:00pm

 

Stationarity is a common assumption in spatial statistics. The justification is often that stationarity is a reasonable approximation to the true state of dependence if we focus on spatial data “locally.” In this talk, we first review various known approaches for modeling nonstationary spatial data. We then examine a particular notion of local stationarity in more detail. To illustrate, we will focus on the multi-fractional Brownian motion, for which a thorough analysis could be conducted assuming data are observed on a regular grid. Finally, extensions to more general settings that relate to Matheron’s intrinsic random functions will be briefly discussed.