Limit theorems for the empirical distribution function in the spatial case.

DOI10.1016/S0167-7152(03)00010-5zbMath1116.60324OpenAlexW1982737658MaRDI QIDQ1423240

István Fazekas

Publication date: 14 February 2004

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-7152(03)00010-5

zbMATH Keywords

Empirical distribution functionRandom fieldMixingFunctional central limit theoremIncreasing domain asymptoticsInfill asymptotics

Mathematics Subject Classification ID

Functional limit theorems; invariance principles (60F17)

Related Items (5)

Asymptotic normality of kernel type density estimators for random fields ⋮ Asymptotic normality of kernel type regression estimators for random fields ⋮ Asymptotic results for spatial causal ARMA models ⋮ On nonparametric variogram estimation ⋮ Practically applicable central limit theorem for spatial statistics

Cites Work

• Asymptotic normality of kernel type density estimators for random fields
• Asymptotic distribution of the empirical spatial cumulative distribution function predictor and prediction bands based on a subsampling method
• On the central limit theorem for nonuniform \(\varphi\)-mixing random fields
• A limit theorem for statistics of spatial data
• Analysing Data from Hormone-Receptor Assays
• Weak convergence of multidimensional empirical processes for strong mixing sequences of stochastic vectors
• Convergence theorems for empirical Lorenz curves and their inverses
• Infill asymptotics inside increasing domains for the least squares estimator in linear models
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