Nonparametric multimodal regression for circular data

Abstract

Multimodal regression estimation methods are introduced for regression models involving circular response and/or covariate. The regression estimators are based on the maximization of the conditional densities of the response variable over the covariate. Conditional versions of the mean shift and the circular mean shift algorithms are used to obtain the regression estimators. The asymptotic properties of the estimators are studied and the problem of bandwidth selection is discussed.

arXiv comment23 pages, 3 figures

References in corpus (7)

• Uniform in bandwidth consistency of kernel-type function estimators
• Nonparametric modal regression
• Kernel density estimation for directional-linear data
• On the Convergence of the Mean Shift Algorithm in the One-Dimensional Space
• Generalized Mode and Ridge Estimation
• Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data
• Fire seasonality identification with multimodality tests