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- W4221010252 abstract "Abstract In 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." @default.
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- W4221010252 date "2022-03-26" @default.
- W4221010252 modified "2023-10-18" @default.
- W4221010252 title "A functional central limit theorem on non-stationary random fields with nested spatial structure" @default.
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- W4221010252 doi "https://doi.org/10.1007/s11203-022-09273-9" @default.
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