Abstract
AbstractIn this paper, we establish a functional central limit theorem on high dimensional random fields in the context of model-based survey analysis. For strongly-mixing non-stationary random fields, we provide an upper bound for the fourth moment of the finite population total. This inequality is the generalization of a key tool for proving functional central limit theorems in Rio (Asymptotic theory of weakly dependent random processes, Springer, Berlin, 2017). Under the nested sampling strategy, we introduce assumptions on strongly-mixing coefficients and quantile functions to show that a functional stochastic process asymptotically approaches to a Gaussian process.
Funder
Marsden
NSERC Strategic Partnership Grant, Canada.
Marsden Fund
Pacific Blue Cross Health Informatics Laboratory, Canada.
Publisher
Springer Science and Business Media LLC
Subject
Statistics and Probability