Search Results - subject:"Random fields"

Tail estimation of the spectral density for a stationary Gaussian random field

Wu, Wei-Ying; Lim, Chae Young; Xiao, Yimin - In: Journal of Multivariate Analysis 116 (2013) C, pp. 74-91

Consider a stationary Gaussian random field on Rd with spectral density f(λ) that satisfies f(λ)∼c|λ|−θ as |λ|→∞. The parameters c and θ control the tail behavior of the spectral density. c is related to a microergodic parameter and θ is related to a fractal index. For data...

Persistent link: https://www.econbiz.de/10011041974

A comparison between Markov approximations and other methods for large spatial data sets

Bolin, David; Lindgren, Finn - In: Computational Statistics & Data Analysis 61 (2013) C, pp. 7-21

The Matérn covariance function is a popular choice for modeling dependence in spatial environmental data. Standard Matérn covariance models are, however, often computationally infeasible for large data sets. Recent results for Markov approximations of Gaussian Matérn fields based on Hilbert...

Persistent link: https://www.econbiz.de/10010617227

Kernel spatial density estimation in infinite dimension space

Dabo-Niang, Sophie; Yao, Anne-Françoise - In: Metrika 76 (2013) 1, pp. 19-52

In this paper, we propose a nonparametric method to estimate the spatial density of a functional stationary random field. This latter is with values in some infinite dimensional normed space and admitted a density with respect to some reference measure. We study both the weak and strong...

Persistent link: https://www.econbiz.de/10010634336

A functional central limit theorem for the level measure of a Gaussian random field

Shashkin, A. - In: Statistics & Probability Letters 83 (2013) 2, pp. 637-643

We prove a functional central limit theorem for the Hausdorff measure of level sets generated by a smooth Gaussian random field.

Persistent link: https://www.econbiz.de/10010602923

Testing for spatial error dependence in probit models

Amaral, Pedro V.; Anselin, Luc; Arribas-Bel, Daniel - In: Letters in spatial and resource sciences : LSRS 6 (2013) 2, pp. 91-101

Persistent link: https://www.econbiz.de/10010194532

Stochastic modelling of the spatial spread of influenza in Germany

Dargatz, Christiane; Georgescu, Vera; Held, Leonhard - 2005

through latent Gaussian Markov random fields (Rue & Held, 2005) with spatio-temporal structure. The main focus will lie on the …

Persistent link: https://www.econbiz.de/10010266160

Adaptive Gaussian Markov random fields with applications in human brain mapping

Brezger, Andreas; Fahrmeir, Ludwig; Hennerfeind, Andrea - 2005

spatial smoothing is based on Gaussian kernels or random fields. A weakness of Gaussian spatial smoothing is underestimation … paper, we introduce a class of inhomogenous Markov random fields (MRF) with spatially adaptive interaction weights in a …

Persistent link: https://www.econbiz.de/10010274234

A functional central limit theorem for empirical processes under a strong mixing condition

Tone, Cristina - In: Statistical Inference for Stochastic Processes 15 (2012) 2, pp. 177-192

Persistent link: https://www.econbiz.de/10010992894

On spatial processes and asymptotic inference under near-epoch dependence

Jenish, Nazgul; Prucha, Ingmar R. - In: Journal of Econometrics 170 (2012) 1, pp. 178-190

literature to random fields. The class of processes that is NED on, say, an α-mixing process, is shown to be closed under … accommodate arrays of random fields on unevenly spaced lattices. The limit theorems are employed to establish consistency and … smaller class of α-mixing processes. The paper then derives a central limit theorem and law of large numbers for NED random …

Persistent link: https://www.econbiz.de/10011052246

Testing the covariance function of stationary Gaussian random fields

Taheriyoun, Ali Reza - In: Statistics & Probability Letters 82 (2012) 3, pp. 606-613

In many problems, a specific function like h(⋅) is considered as the covariance function. Based on the asymptotic distribution of the periodogram and Euler characteristic, three methods are introduced to test the equality of the covariance function with h(⋅). Our analyses prove the accuracy...

Persistent link: https://www.econbiz.de/10011039778