Nonparametric Regression in Nonstandard Spaces
From MaRDI portal
Publication:6356902
DOI10.1214/22-EJS2056arXiv2012.13332MaRDI QIDQ6356902
Publication date: 24 December 2020
Abstract: A nonparametric regression setting is considered with a real-valued covariate and responses from a metric space. One may approach this setting via Fr'echet regression, where the value of the regression function at each point is estimated via a Fr'echet mean calculated from an estimated objective function. A second approach is geodesic regression, which builds upon fitting geodesics to observations by a least squares method. These approaches are applied to transform two of the most important nonparametric regression estimators in statistics to the metric setting -- the local linear regression estimator and the orthogonal series projection estimator. The resulting procedures consist of known estimators as well as new methods. We investigate their rates of convergence in a general setting and compare their performance in a simulation study on the sphere.
This page was built for publication: Nonparametric Regression in Nonstandard Spaces
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6356902)
