The construction of variance estimators for particulate material sampling

TL;DR

This paper develops variance estimators for particulate material sampling, incorporating particle masses, concentrations, and dependent selection parameters to improve estimation accuracy.

Contribution

It introduces new variance estimators, including hybrid classes, for better sampling variance estimation in particulate materials.

Findings

New variance estimators constructed for particulate sampling.

Hybrid estimators demonstrate improved accuracy.

Method applicable to dependent particle selection scenarios.

Abstract

The variance of the concentration in a sample can be estimated using knowledge of the particle masses, concentrations and the parameter for the dependent selection of particles. A number of variance estimators are constructed including a class of hybrid estimators.