Lp-Adaptive Estimation Under Partially Linear Constraint in Regression Model
Kouame Florent Kouakou and
Armel Fabrice Evrard Yode
Journal of Mathematics Research, 2020, vol. 12, issue 6, 74
We study the problem of multivariate estimation in the nonparametric regression model with random design. We assume that the regression function to be estimated possesses partially linear structure, where parametric and nonparametric components are both unknown. Based on Goldenshulger and Lepski methodology, we propose estimation procedure that adapts to the smoothness of the nonparametric component, by selecting from a family of specific kernel estimators. We establish a global oracle inequality (under the Lp-norm, 1≤p＜1) and examine its performance over the anisotropic H¨older space.
JEL-codes: R00 Z0 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ibn:jmrjnl:v:12:y:2020:i:6:p:74
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