Approximations for hybrids of empirical and partial sums processes

DOI10.1016/S0378-3758(99)00207-4zbMath0976.60044OpenAlexW2037530690WikidataQ127673875 ScholiaQ127673875MaRDI QIDQ1578213

Lajos Horváth

Publication date: 2 January 2002

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(99)00207-4

zbMATH Keywords

empirical processesPoisson approximationsGaussian approximationsrandomly stopped partial sums

Mathematics Subject Classification ID

Order statistics; empirical distribution functions (62G30) Functional limit theorems; invariance principles (60F17)

Related Items (17)

Strong approximations for weighted bootstrap of empirical and quantile processes with applications ⋮ Detections of changes in return by a wavelet smoother with conditional heteroscedastic volatility ⋮ On the Local Time of the Weighted Bootstrap and Compound Empirical Processes ⋮ Almost sure central limit theorem for the hybrid process ⋮ A simple approach to construct confidence bands for a regression function with incomplete data ⋮ On a weighted bootstrap approximation of the \(L_p\) norms of kernel density estimators ⋮ Approximations for a multivariate hybrid process with applications to change-point detection ⋮ On the performance of weighted bootstrapped kernel deconvolution density estimators ⋮ Asymptotic results for hybrids of empirical and partial sums processes ⋮ Some asymptotic properties of the hybrids of empirical and partial-sum processes ⋮ On the hybrids of \(k\)-spacing empirical and partial sum processes ⋮ On some functional of the hybrid process in random scenery ⋮ Approximations for weighted bootstrap processes with an application ⋮ On the maximal deviation of kernel regression estimators with NMAR response variables ⋮ Goodness-of-fit testing based on a weighted bootstrap: A fast large-sample alternative to the parametric bootstrap ⋮ On Complete Convergence for the Hybrid Process ⋮ Weighted bootstrapped kernel density estimators in two-sample problems

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