• Bernoulli
  • Volume 25, Number 2 (2019), 1289-1325.

Minimax optimal estimation in partially linear additive models under high dimension

Zhuqing Yu, Michael Levine, and Guang Cheng

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In this paper, we derive minimax rates for estimating both parametric and nonparametric components in partially linear additive models with high dimensional sparse vectors and smooth functional components. The minimax lower bound for Euclidean components is the typical sparse estimation rate that is independent of nonparametric smoothness indices. However, the minimax lower bound for each component function exhibits an interplay between the dimensionality and sparsity of the parametric component and the smoothness of the relevant nonparametric component. Indeed, the minimax risk for smooth nonparametric estimation can be slowed down to the sparse estimation rate whenever the smoothness of the nonparametric component or dimensionality of the parametric component is sufficiently large. In the above setting, we demonstrate that penalized least square estimators can nearly achieve minimax lower bounds.

Article information

Bernoulli, Volume 25, Number 2 (2019), 1289-1325.

Received: June 2017
Revised: January 2018
First available in Project Euclid: 6 March 2019

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high dimension minimax optimal partial linear additive model semiparametric


Yu, Zhuqing; Levine, Michael; Cheng, Guang. Minimax optimal estimation in partially linear additive models under high dimension. Bernoulli 25 (2019), no. 2, 1289--1325. doi:10.3150/18-BEJ1021.

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