Asymptotic Spectral Theory for Spatial Data

05/27/2020
by   Wai Leong Ng, et al.
0

In this paper we study the asymptotic theory for spectral analysis in stationary random fields, including linear and nonlinear fields. Asymptotic properties of Fourier coefficients and periodograms, including limiting distributions of Fourier coefficients, and uniform consistency of kernel spectral density estimators are obtained under various conditions on moments and weak dependence structures. The validity of the aforementioned asymptotic results for estimated spatial fields is also established.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/21/2020

Central limit theorems for stationary random fields under weak dependence with application to ambit and mixed moving average fields

We obtain central limit theorems for stationary random fields which are ...
research
09/03/2020

Spectral estimation for spatial point patterns

This article determines how to implement spatial spectral analysis of po...
research
10/08/2020

Estmiation of the Spectral Measure from Convex Combinations of Regularly Varying Random Vectors

The extremal dependence structure of a regularly varying random vector X...
research
10/02/2019

Trigonometric splines in spectral problems

Some questions of application of trigonometric splines in problems of sp...
research
11/24/2022

Local polynomial regression for spatial data on ℝ^d

This paper develops a general asymptotic theory of local polynomial (LP)...
research
07/13/2013

MCMC Learning

The theory of learning under the uniform distribution is rich and deep, ...
research
06/28/2021

A deep look into the Dagum family of isotropic covariance functions

The Dagum family of isotropic covariance functions has two parameters th...

Please sign up or login with your details

Forgot password? Click here to reset