Exclusion of measurements with excessive residuals (blunders) in estimating model parameters

TL;DR

This paper introduces an adaptive algorithm for excluding measurements with large residuals in model parameter estimation, improving robustness and convergence in data analysis.

Contribution

It presents a novel, easy-to-use exclusion algorithm with variable limits that adapt as equations are removed, enhancing robustness and efficiency.

Findings

The algorithm converges rapidly.

It reduces subjectivity in data exclusion.

It is highly general and adaptable.

Abstract

An adjustable algorithm of exclusion of conditional equations with excessive residuals is proposed. The criteria applied in the algorithm use variable exclusion limits which decrease as the number of equations goes down. The algorithm is easy to use, it possesses rapid convergence, minimal subjectivity, and high degree of generality.