Work

An entropy-based approach for a robust least squares spline approximation

Year: 2024

Type: article

Abstract: We consider the weighted least squares spline approximation of a noisy dataset. By interpreting the weights as a probability distribution, we maximize the associated entropy subject to the constraint that the mean squared error is prescribed to a desired (small) value. Acting on this error yields a robust regression method that automatically detects and removes outliers from the data during the fitting procedure, by assigning them a very small weight. We discuss the use of both spline functions and spline curves. A number of numerical illustrations have been included to disclose the potentialities of the maximal-entropy approach in different application fields.

Source: Journal of Computational and Applied Mathematics

DOI: 10.1016/j.cam.2024.115773

Authors: Luigi Brugnano, Domenico Giordano, Felice Iavernaro, Giorgia Rubino

Institutions: University of Florence, European Space Agency, European Space Research and Technology Centre, University of Bari Aldo Moro

Language: English

Topic: Statistical and numerical algorithms

Subfield: Applied Mathematics

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,160

Funders: Istituto Nazionale di Alta Matematica "Francesco Severi", European Commission, Gruppo Nazionale per il Calcolo Scientifico, NextGenerationEU