A0227
Title: A flexible bias correction method based on inconsistent estimators
Authors: Stephane Guerrier - University of Geneva (Switzerland)
Mucyo Karemera - University of Geneva (Switzerland) [presenting]
Samuel Orso - University of Geneva (Switzerland)
Maria-Pia Victoria-Feser - University of Geneva (Switzerland)
Yuming Zhang - University of Geneva (Switzerland)
Yanyuan Ma - Penn State University (United States)
Abstract: An important challenge in statistical analysis lies in controlling the estimation bias when handling the ever-increasing data size and model complexity. For example, approximate methods are increasingly used to address the analytical and/or computational challenges when implementing standard estimators, but they often lead to inconsistent estimators. So consistent estimators can be difficult to obtain, especially for complex models and/or in settings where the number of parameters diverges with the sample size. We propose a general simulation-based estimation framework that allows the construction of consistent and bias-corrected estimators for increasing dimension parameters. The key advantage of the proposed framework is that it only requires computing a simple inconsistent estimator multiple times. The resulting Just Identified iNdirect Inference estimator (JINI) enjoys nice properties, including consistency, asymptotic normality, and finite sample bias correction, better than alternative methods. We further provide a simple algorithm to construct the JINI in a computationally efficient manner. Therefore, the JINI is especially useful in settings where standard methods may be challenging to apply, for example, in the presence of misclassification and rounding. We consider comprehensive simulation studies and analyze an alcohol consumption data example to illustrate the excellent performance and usefulness of the method.
