Local polynomial regression for circular predictors
By M. Di Marzio, A. Panzera, Charles C. Taylor. 2009
74 citations recorded by the source corpus.
We consider local smoothing of datasets where the design space is the d-dimensional (d>=1) torus and the response variable is real-valued. Our purpose is to extend least squares local polynomial fitting to this situation. We give both theoretical and empirical results.
Subfields: Applied Mathematics, Computational Mechanics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/d11300e7c1cfb70b7b8f90a3b96a3d962cb9d4ea
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In Applied Mathematics
In Computational Mechanics
In Statistics and Probability
Citations in the corpus
- Exact risk improvement of bandwidth selectors for kernel density estimation with directional data (2013, methodology): “First, the AMISE expansion that Di Marzio et al. (2009) derived for the circular kernel density estimator by the use of Fourier expansions of the circular kernels.”
- Nonparametric Regression for Spherical Data (2014, methodology): “For the special case when the predictor is defined on the circle, a distinct nonparametric regression was proposed by Di Marzio, Panzera, and Taylor (2009).”
- Non‐parametric Regression for Circular Responses (2013, background): “Non-parametric smoothing is aimed to estimate, by local averaging, a conditional mean response m which is not assumed to belong to a known parametric family, only a few smoothness assumptions being required.”