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An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

How to avoid over-fitting in multivariate calibration--the conventional validation approach and an alternative.

N M Faber¹, R Rajkó

¹Chemometry Consultancy, Rubensstraat 7, 6717 VD Ede, The Netherlands. nmf@chemometry.com

Analytica Chimica Acta

|July 4, 2007

Summary

Over-fitting in multivariate calibration is a challenge. A new randomization test offers a more objective way to assess model components compared to traditional validation methods.

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Area of Science:

Chemometrics
Statistical Modeling
Analytical Chemistry

Background:

Over-fitting is a common problem in multivariate calibration, leading to models that do not generalize well.
Conventional validation methods like cross-validation and independent test sets can be subjective and rely on 'soft' decision rules.

Purpose of the Study:

To critically review over-fitting in multivariate calibration.
To propose and evaluate a novel randomization test for assessing the statistical significance of model components.
To compare this alternative approach with traditional validation techniques.

Main Methods:

A randomization test was developed to assess the statistical significance of components in a multivariate model.
This method was compared against cross-validation and independent test set validation.
The methods were applied to a near-infrared spectral data set using partial least squares (PLS) regression.

Main Results:

The randomization test provides a more objective assessment of model components compared to validation-based approaches.
Unlike validation methods, the randomization test avoids the need for subjective 'soft' decision rules.
The study demonstrated the effectiveness of the randomization test in the context of PLS regression for spectral data.

Conclusions:

The proposed randomization test is a valuable and objective tool for multivariate calibration.
It offers a statistically rigorous alternative to conventional validation techniques.
This method enhances the reliability of chemometric models by providing objective significance assessments.