Precision Profile Weighted Deming Regression for Methods Comparison
Douglas M Hawkins, Jessica J Kraker
TL;DR
This paper introduces a methodology that combines precision profile models with Deming regression to improve methods comparison involving measurements with nonconstant variance, supported by R routines.
Contribution
It presents a novel approach integrating precision profile models with Deming regression, along with R routines for practical implementation.
Findings
Effective weighting for nonconstant variance in Deming regression
Enhanced accuracy in methods comparison across measurement ranges
Provision of R routines for practical application
Abstract
Errors in variables (Deming) regression of measurements spanning a wide range of values requires appropriate weighting to reflect nonconstant variance. Precision profile models, mathematical relationships between measurement variance and mean, are a route to these weights. The paper describes a methodology combining general precision profile models with Deming regression and described R routines for the resulting calculations.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.