Open Access
2023 Kernel machines with missing covariates
Tiantian Liu, Yair Goldberg
Author Affiliations +
Electron. J. Statist. 17(2): 2485-2538 (2023). DOI: 10.1214/23-EJS2158


We develop a family of doubly robust kernel machines for classification in the presence of missing covariates. We assume that the missingness is missing at random and the missing pattern is homogeneous over a subset of covariates. First, we construct a novel convex augmented loss function using inverse probability weighting, multiple imputation, and surrogacy. It features (i) the double robustness against misspecification of the missing mechanism or the imputation model, and (ii) computation feasibility via a constrained quadratic optimization. Second, we obtain theoretical results for the proposed kernel machine, which include Fisher consistency, an upper bound of the excess risk, and the rate of convergence. We demonstrate the finite sample performance of the proposed kernel machine through simulation and real data analysis.

Funding Statement

Yair Goldberg was partially supported by the Israeli Science Foundation (grant No. 849/17).


Download Citation

Tiantian Liu. Yair Goldberg. "Kernel machines with missing covariates." Electron. J. Statist. 17 (2) 2485 - 2538, 2023.


Received: 1 November 2022; Published: 2023
First available in Project Euclid: 26 October 2023

MathSciNet: MR4660701
Digital Object Identifier: 10.1214/23-EJS2158

Primary: 60K35

Keywords: ‎classification‎ , doubly robust estimators , Kernel machines , missing covariates , multiple imputation

Vol.17 • No. 2 • 2023
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