A tunable parameter in Lasso controls the sparsity of the estimated vector, and the number of outliers as a byproduct. Hence, effective methods to select this ...

Abstract: Nonparametric methods are widely applicable to statistical learning problems, since they rely on a few modeling assumptions.

Nonparametric methods are widely applicable to statistical learning problems, since they rely on a few modeling assumptions. In this context, the fresh look ...

Apr 4, 2011 · Outliers are identified by judiciously tuning regularization parameters, which amounts to controlling the sparsity of the outlier vector along ...

The sparsity-controlling robust nonparametric regression methods of this paper are compared with the SVR [43] and robust SVR in [10], for the case of the ...

Robust Nonparametric Regression via Sparsity Control With Application to Load Curve Data Cleansing · G. Mateos, G. Giannakis · Published in IEEE Transactions on ...

The novel robust spline-based smoother is adopted to cleanse load curve data, a key task aiding operational decisions in the envisioned smart grid system.

Dec 5, 2024 · Nonparametric methods are widely applicable to statistical inference problems, since they rely on a few modeling assumptions.

Dive into the research topics of 'Robust nonparametric regression via sparsity control with application to load curve data cleansing'. Together they form a ...

Apr 4, 2011 · Nonparametric methods are widely applicable to statistical inference problems, since they rely on a few modeling assumptions.