On LSE in regression model for long-range dependent random fields on spheres

05/22/2019
by   Vo Anh, et al.
0

We study the asymptotic behaviour of least squares estimators in regression models for long-range dependent random fields observed on spheres. The least squares estimator can be given as a weighted functional of long-range dependent random fields. It is known that in this scenario the limits can be non-Gaussian. We derive the limit distribution and the corresponding rate of convergence for the estimators. The results were obtained under rather general assumptions on the random fields. Simulation studies were conducted to support theoretical findings.

READ FULL TEXT

page 17

page 18

research
05/01/2020

Reduction principle for functionals of strong-weak dependent vector random fields

We prove the reduction principle for asymptotics of functionals of vecto...
research
12/18/2018

Limit theorems for filtered long-range dependent random fields

This article investigates general scaling settings and limit distributio...
research
05/24/2019

Asymptotic Behaviour of Discretised Functionals of Long-Range Dependent Functional Data

The paper studies the asymptotic behaviour of weighted functionals of lo...
research
09/12/2017

Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields

Despite successful applications across a broad range of NLP tasks, condi...
research
02/13/2019

Estimation of causal CARMA random fields

We estimate model parameters of Lévy-driven causal CARMA random fields b...
research
06/30/2015

Forming A Random Field via Stochastic Cliques: From Random Graphs to Fully Connected Random Fields

Random fields have remained a topic of great interest over past decades ...

Please sign up or login with your details

Forgot password? Click here to reset