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Updated: Aug 16, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Automatic uncertainty evaluation for determining the number of components in nested models and the shrinkage
Luca Martino1, Roberto San Millán-Castillo2, Eduardo Morgado2
1Universitá degli studi di Catania, Catania, Italy.
Plos One
|August 14, 2026
Summary
This study introduces two novel procedures for creating uncertainty intervals for model selection and regularization parameters. These broadly applicable methods, leveraging error curve geometry, provide plausible integer bounds without requiring a likelihood function.
Area of Science:
- Statistics
- Machine Learning
- Data Science
Background:
- Model selection and regularization involve determining key parameters like the number of components or shrinkage values.
- Quantifying the uncertainty in these parameter estimates is crucial for reliable model interpretation and application.
- Existing methods often depend on likelihood functions, limiting their applicability.
Purpose of the Study:
- To propose and evaluate two new procedures for constructing uncertainty intervals.
- To provide plausible integer bounds for the number of components or shrinkage parameters.
- To develop methods broadly applicable across diverse machine learning domains.
Main Methods:
- The study develops two procedures based on the geometric properties of the error curve.
- These methods construct intervals defined by two integer bounds.
- They do not require a likelihood function for their operation.
Main Results:
- The proposed procedures effectively capture uncertainty in parameter estimation.
- Extensive experiments on synthetic and real-world datasets confirm their utility.
- MATLAB code is provided for practical implementation.
Conclusions:
- The novel interval construction methods offer a robust approach to uncertainty quantification.
- Their independence from likelihood functions enhances their applicability in various statistical and machine learning tasks.
- The provided code facilitates adoption by researchers and practitioners for improved model selection and regularization.
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