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Main Authors: Pan, Yue, Pan, Jiazhu
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2408.07429
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author Pan, Yue
Pan, Jiazhu
author_facet Pan, Yue
Pan, Jiazhu
contents We obtain the law of large numbers (LLN) and the central limit theorem (CLT) for weakly dependent non-stationary arrays of random fields with asymptotically unbounded moments. The weak dependence condition for arrays of random fields is proved to be inherited through transformation and infinite shift. This paves a way to prove the consistency and asymptotic normality of maximum likelihood estimation for dynamic spatio-temporal models (i.e. so-called ultra high-dimensional time series models) when the sample size and/or dimension go to infinity. Especially the asymptotic properties of estimation for network autoregression are obtained under reasonable regularity conditions.
format Preprint
id arxiv_https___arxiv_org_abs_2408_07429
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Limit Theorems for Weakly Dependent Non-stationary Random Field Arrays and Asymptotic Inference of Dynamic Spatio-temporal Models
Pan, Yue
Pan, Jiazhu
Statistics Theory
We obtain the law of large numbers (LLN) and the central limit theorem (CLT) for weakly dependent non-stationary arrays of random fields with asymptotically unbounded moments. The weak dependence condition for arrays of random fields is proved to be inherited through transformation and infinite shift. This paves a way to prove the consistency and asymptotic normality of maximum likelihood estimation for dynamic spatio-temporal models (i.e. so-called ultra high-dimensional time series models) when the sample size and/or dimension go to infinity. Especially the asymptotic properties of estimation for network autoregression are obtained under reasonable regularity conditions.
title Limit Theorems for Weakly Dependent Non-stationary Random Field Arrays and Asymptotic Inference of Dynamic Spatio-temporal Models
topic Statistics Theory
url https://arxiv.org/abs/2408.07429