Local likehood regression estimation for circular covariates
M. Alonso-Pena, I. Gijbels, R. M. Crujeiras
Circular data are periodic observations that can be regarded as points on a unit circumference. These types of data are found in a many different fields such as psuchology, meteorology or biology, and can be found jointly with other real-valued variables. We propose a general approach that allows to estimate, nonparametrically, regression functions with a circular predictor and a general response, which can be either a continuous or a discrete variable. The estimation consists of hte maximization of a circular kernel weighted log-likelihood. The obtainment of the bias and variance of the estimators is also addressed, as well as the problemn of the selection of the smoothing parameter. The proposal will be illustrated with some real data applications.
Keywords: circular data, data-driven smoothing selection, kernel regression, local likelihood
Scheduled
GT18 Non-parametric statistics III. Nonparametric inference for regression
June 9, 2022 5:10 PM
A15
Other papers in the same session
F. J. Navas Gómez, M. L. Gámiz Pérez, R. Raya Miranda
J. C. Pardo-Fernández, G. Boente
J. Ameijeiras-Alonso, I. Gijbels
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6/8/22
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