A General Approach for Simulation-based Bias Correction in High Dimensional Settings

TL;DR

This paper introduces a flexible simulation-based framework for bias correction in high-dimensional statistical models, improving estimator accuracy and consistency in complex data scenarios.

Contribution

It proposes a general, computationally efficient method for bias correction that outperforms existing approaches in high-dimensional settings.

Findings

Stronger bias correction properties demonstrated.

Applicable to models with censoring and misclassification.

Framework is easy to implement and computationally efficient.

Abstract

An important challenge in statistical analysis lies in controlling the bias of estimators due to the ever-increasing data size and model complexity. Approximate numerical methods and data features like censoring and misclassification often result in analytical and/or computational challenges when implementing standard estimators. As a consequence, consistent estimators may be difficult to obtain, especially in complex and/or high dimensional settings. In this paper, we study the properties of a general simulation-based estimation framework that allows to construct bias corrected consistent estimators. We show that the considered approach leads, under more general conditions, to stronger bias correction properties compared to alternative methods. Besides its bias correction advantages, the considered method can be used as a simple strategy to construct consistent estimators in settings where alternative methods may be challenging to apply. Moreover, the considered framework can be easily implemented and is computationally efficient. These theoretical results are highlighted with simulation studies of various commonly used models, including the negative binomial regression (with and without censoring) and the logistic regression (with and without misclassification errors). Additional numerical illustrations are provided in the supplementary materials.