Semiparametric Estimation of a Noise Model with Quantization Errors

TL;DR

This paper develops an optimal estimator for the gain parameter in mass spectrometry detectors affected by quantization errors, enabling better analysis of ion counts when the gain exceeds one.

Contribution

It introduces a novel estimator for the gain parameter in a semiparametric noise model, effective when the gain is greater than one, regardless of ion count distribution.

Findings

Estimator is optimal for t > 1

Enables analysis without prior N information

Addresses quantization errors in mass spectrometry

Abstract

The detectors in mass spectrometers are precise enough to count ion events. In practice, the statistics of chemical noise are affected by large quantization errors and overdispersion because of amplification in the detector. The detector signal is modelled as X =floor(t N) where N represents integer-valued ion counts and t represents the gain parameter. When t <= 1, the gain parameter cannot be recovered without a priori information on N. When t > 1 however, we introduce compatible lattices and derive an estimator for t that is optimal, independent of N and enables subsequent analyses of the ion counts.