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Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

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Fused Lasso Additive Model.

Ashley Petersen¹, Daniela Witten¹, Noah Simon¹

¹Department of Biostatistics, University of Washington, Seattle WA 98195.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America

|February 28, 2017

Summary

This summary is machine-generated.

The fused lasso additive model (FLAM) offers flexible, interpretable predictions for outcome variables using covariates. This new statistical method provides consistent high-dimensional estimation and an unbiased degrees of freedom estimator.

Keywords:

additive model feature selection high-dimensional non-parametric regression piecewise constant sparsity

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Area of Science:

Statistics
Machine Learning
Statistical Modeling

Background:

Predicting outcome variables with covariates is crucial in statistical analysis.
Existing methods may lack flexibility, interpretability, or handle high-dimensional data poorly.

Purpose of the Study:

To introduce the fused lasso additive model (FLAM) for flexible, interpretable, and accurate outcome prediction.
To develop a method that performs well in high-dimensional settings.

Main Methods:

FLAM estimates additive functions as piecewise constant with adaptively chosen knots.
The model is solved via convex optimization with a guaranteed convergent algorithm.
An unbiased degrees of freedom estimator is proposed for FLAM.

Main Results:

FLAM demonstrates consistency in high-dimensional settings.
The method provides a flexible and interpretable approach to additive modeling.
Performance was evaluated through simulations and real-world datasets.

Conclusions:

FLAM is a robust statistical approach for outcome prediction.
The method offers a balance of flexibility, interpretability, and statistical rigor.
An R package is available for practical implementation.