Trapezoidal rule and sampling designs for the nonparametric estimation of the regression function in models with correlated errors

DOI10.1080/02331888.2020.1715409zbMath1436.62128arXiv1806.04896OpenAlexW3002912040WikidataQ126304520 ScholiaQ126304520MaRDI QIDQ5213358

Sana Louhichi, D. Benelmadani, Karim Benhenni

Publication date: 3 February 2020

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1806.04896

zbMATH Keywords

asymptotic normalityautocovariance functionnonparametric regressionoptimal designtrapezoidal rule

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Optimal statistical designs (62K05)

Related Items (4)

On sufficient conditions for the consistency of local linear kernel estimators ⋮ Towards Insensitivity of Nadaraya--Watson Estimators to Design Correlation ⋮ Universal kernel-type estimation of random fields ⋮ The reproducing kernel Hilbert space approach in nonparametric regression problems with correlated observations

Uses Software

• GenSA

Cites Work

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