Invariance principles for deconvolving kernel density estimation for stationary sequences of random variables

DOI10.1016/S0378-3758(99)00162-7zbMath0952.62030OpenAlexW1978687910WikidataQ126573640 ScholiaQ126573640MaRDI QIDQ1973314

Stergios B. Fotopoulos

Publication date: 27 April 2000

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)00162-7

zbMATH Keywords

strong convergenceweak convergencedeconvolutionkernel density estimatorsFourier transformation

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Non-Markovian processes: estimation (62M09) Order statistics; empirical distribution functions (62G30) Functional limit theorems; invariance principles (60F17)

Related Items (3)

Optimal iterative density deconvolution ⋮ Low Order Approximations in Deconvolution and Regression with Errors in Variables ⋮ Nonparametric deconvolution of density estimation based on observed sums

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