On the \(L_p\) norms of kernel regression estimators for incomplete data with applications to classification

DOI10.1007/s10260-016-0359-6zbMath1397.62151OpenAlexW2461815745MaRDI QIDQ518885

Timothy Reese, Majid Mojirsheibani

Publication date: 30 March 2017

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10260-016-0359-6

zbMATH Keywords

strong convergencekernelregression functionselection probabilityincomplete covariatesnonparametric estimatorstatistical classification

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

Classification with incomplete functional covariates, Rates of the strong uniform consistency for the kernel-type regression function estimators with general kernels on manifolds, Multivariate wavelet estimators for weakly dependent processes: strong consistency rate, On classification with nonignorable missing data

Uses Software

• np
• hgam

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