Non-parametric regression estimation on closed Riemannian manifolds

By Bruno Pelletier. 2006

69 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/055c9988a68d95175c9e40f7207492b8524b0bc3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nengxiang Ling
2. M. Di Marzio
3. A. Panzera
4. Charles C. Taylor
5. H. Muller
6. Alexander Petersen
7. Philippe Vieu
8. Heng Lian

In Environmental Engineering

1. Philippe Vieu
2. M. Di Marzio
3. Nengxiang Ling
4. Alexander Petersen
5. H. Muller
6. A. Panzera
7. Charles C. Taylor

In Geometry and Topology

1. Alexander Petersen
2. H. Muller
3. Philippe Vieu
4. Nengxiang Ling
5. Piotr Kokoszka

Citations in the corpus

• Nonparametric modelling for functional data: selected survey and tracks for future (2018, result): “Similar results are presented in [21] for the special case when the space F is a Rieman-typemanifold.”
• Fréchet regression for random objects with Euclidean predictors (2016, background): “…regression (Fletcher 2013; Niethammer, Huang and Vialard 2011; Cornea et al. 2016), semiparametric regression (Shi et al. 2009) and local kernel regression as a generalization of the classical Nadaraya-Watson smoother (Pelletier 2006; Davis et al. 2007; Hinkle et al. 2012; Yuan et al. 2012).”
• Nonparametric Regression for Spherical Data (2014, background): “Specifically, Pelletier (2006) defined a Nadaraya–Watsonlike estimate for this setting in a fully theoretical fashion.”