# Phase Function Density Deconvolution with Heteroscedastic Measurement Error of Unknown Type

## TL;DR

This paper introduces a novel phase function density deconvolution method that effectively handles heteroscedastic measurement errors of unknown distribution, improving estimation accuracy in biomedical data analysis.

## Contribution

It develops a weighted empirical phase function approach for heteroscedastic errors with unknown distribution, extending existing methods to more realistic biomedical scenarios.

## Key findings

- Weighted phase function reduces mean integrated squared error
- Estimation of weights from replicates is effective
- Method is competitive with existing approaches

## Abstract

It is important to properly correct for measurement error when estimating density functions associated with biomedical variables. These estimators that adjust for measurement error are broadly referred to as density deconvolution estimators. While most methods in the literature assume the distribution of the measurement error to be fully known, a recently proposed method based on the empirical phase function (EPF) can deal with the situation when the measurement error distribution is unknown. The EPF density estimator has only been considered in the context of additive and homoscedastic measurement error; however, the measurement error of many biomedical variables is heteroscedastic in nature. In this paper, we developed a phase function approach for density deconvolution when the measurement error has unknown distribution and is heteroscedastic. A weighted empirical phase function (WEPF) is proposed where the weights are used to adjust for heteroscedasticity of measurement error. The asymptotic properties of the WEPF estimator are evaluated. Simulation results show that the weighting can result in large decreases in mean integrated squared error (MISE) when estimating the phase function. The estimation of the weights from replicate observations is also discussed. Finally, the construction of a deconvolution density estimator using the WEPF is compared to an existing deconvolution estimator that adjusts for heteroscedasticity, but assumes the measurement error distribution to be fully known. The WEPF estimator proves to be competitive, especially when considering that it relies on the minimal assumption of the distribution of measurement error.