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October 2016 Local intrinsic stationarity and its inference
Tailen Hsing, Thomas Brown, Brian Thelen
Ann. Statist. 44(5): 2058-2088 (October 2016). DOI: 10.1214/15-AOS1402


Dense spatial data are commonplace nowadays, and they provide the impetus for addressing nonstationarity in a general way. This paper extends the notion of intrinsic random function by allowing the stationary component of the covariance to vary with spatial location. A nonparametric estimation procedure based on gridded data is introduced for the case where the covariance function is regularly varying at any location. An asymptotic theory is developed for the procedure on a fixed domain by letting the grid size tend to zero.


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Tailen Hsing. Thomas Brown. Brian Thelen. "Local intrinsic stationarity and its inference." Ann. Statist. 44 (5) 2058 - 2088, October 2016.


Received: 1 October 2014; Revised: 1 September 2015; Published: October 2016
First available in Project Euclid: 12 September 2016

zbMATH: 1360.62475
MathSciNet: MR3546443
Digital Object Identifier: 10.1214/15-AOS1402

Primary: 60G12 , 62G05 , 62G20 , 62M30

Keywords: Intrinsic random functions , nonparametric estimation , nonstationary spatial process

Rights: Copyright © 2016 Institute of Mathematical Statistics


Vol.44 • No. 5 • October 2016
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