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

Regresión isotónica logística para el cálculo de estructuras en fiabilidad

F. J. Navas Gómez, M. L. Gámiz Pérez, R. Raya Miranda

On a semiparametric circular regression model

J. Ameijeiras-Alonso, I. Gijbels


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