Optimal Estimation of Slope Vector in High-dimensional Linear Transformation Model

TL;DR

This paper introduces CENet, a convex optimization-based method for estimating the slope vector and performing variable selection in high-dimensional linear transformation models, achieving optimal convergence rates.

Contribution

CENet is the first method to attain optimal convergence rates for slope estimation in high-dimensional linear transformation models with a convex optimization approach.

Findings

CENet achieves the same optimal convergence rate as the best high-dimensional regression methods.

CENet performs well on both simulated and real datasets.

The method has guaranteed convergence to the global optimum.

Abstract

In a linear transformation model, there exists an unknown monotone nonlinear transformation function such that the transformed response variable and the predictor variables satisfy a linear regression model. In this paper, we present CENet, a new method for estimating the slope vector and simultaneously performing variable selection in the high-dimensional sparse linear transformation model. CENet is the solution to a convex optimization problem and can be computed efficiently from an algorithm with guaranteed convergence to the global optimum. We show that under a pairwise elliptical distribution assumption on each predictor-transformed-response pair and some regularity conditions, CENet attains the same optimal rate of convergence as the best regression method in the high-dimensional sparse linear regression model. To the best of our limited knowledge, this is the first such result in the literature. We demonstrate the empirical performance of CENet on both simulated and real datasets. We also discuss the connection of CENet with some nonlinear regression/multivariate methods proposed in the literature.