Extending the Patra-Sen Approach to Estimating the Background Component in a Two-Component Mixture Model

TL;DR

This paper extends the Patra-Sen approach for estimating background components in two-component mixture models to settings where the background is symmetric, monotonic, or log-concave, providing consistent estimators and confidence bands.

Contribution

It introduces new estimators for background components under additional shape constraints and demonstrates their consistency and practical implementation.

Findings

Effective estimation under shape constraints

Less prior knowledge needed compared to existing methods

Validated on synthetic and real datasets

Abstract

Patra and Sen (2016) consider a two-component mixture model, where one component plays the role of background while the other plays the role of signal, and propose to estimate the background component by simply "maximizing" its weight. While in their work the background component is a completely known distribution, we extend their approach here to three emblematic settings: when the background distribution is symmetric; when it is monotonic; and when it is log-concave. In each setting, we derive estimators for the background component, establish consistency, and provide a confidence band. While the estimation of a background component is straightforward when it is taken to be symmetric or monotonic, when it is log-concave its estimation requires the computation of a largest concave minorant, which we implement using sequential quadratic programming. Compared to existing methods, our method has the advantage of requiring much less prior knowledge on the background component, and is thus less prone to model misspecification. We illustrate this methodology on a number of synthetic and real datasets.