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Andrea Meilán, Universidad de A Coruña


Nonparametric inference for linear-circular regression models with independent and spatially correlated errors


Circular data can be regarded as points whose support is on a circle (with unit radius) measured in degrees or radians, and with periodic nature. Examples of circular data arise in many applied fields such as biology, meteorology or oceanography, among others. Sometimes, these data may be spatially correlated, that is, close observations tend to be more similar than those that are far apart. Therefore, such observations cannot be treated as independent and the dependence structure should be taken into account in any inferential procedure.

The goal of this work is to design and study new nonparametric regression function estimation approaches and goodness-of-fit tests for models with a circular response and an $R^d$-valued covariate, under the assumption of independence and also for spatially correlated errors. Comprehensive simulation studies and application of the different methodologies to real datasets complete this work.