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Statistics Seminar - Tailen Hsing, University of Michigan - "Modeling and Inference of Local Stationarity"

Event Type
106B1 - Engineering Hall
Nov 30, 2017   3:30 pm  
Originating Calendar
Department of Statistics Event Calendar

Title: Modeling and Inference of Local Stationarity

Abstract: 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 the 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.

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