Some numerical test on the convergence rates of regression with differential regularization
Keywords
Statistical learning
Code:
53/2020
Title:
Some numerical test on the convergence rates of regression with differential regularization
Date:
Thursday 23rd July 2020
Author(s):
Arnone, E.; Kneip, A.; Nobile, F.; Sangalli, L. M.
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Abstract:
We numerically study the bias and the mean square error of the estimator in Spatial Regression with Partial Differential Equation (SR-PDE) regularization.
SR-PDE is a novel smoothing technique for data distributed over two-dimensional domains, which allows to incorporate prior information formalized in term of a partial differential equation. This technique also enables an accurate estimation when the shape of the domain is complex and it strongly influences the phenomenon under study.
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Arnone, E., Kneip, A., Nobile, F., Sangalli, L. M. (2020, June). Some Numerical Test on the Convergence Rates of Regression with Differential Regularization. In International Workshop on Functional and Operatorial Statistics (pp. 11-18). Springer, Cham.
Arnone, E., Kneip, A., Nobile, F., Sangalli, L. M. (2020, June). Some Numerical Test on the Convergence Rates of Regression with Differential Regularization. In International Workshop on Functional and Operatorial Statistics (pp. 11-18). Springer, Cham.