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Nonparametric estimation for a functional-circular regression model Articles uri icon

publication date

  • April 2024

start page

  • 945

end page

  • 974

volume

  • 65

International Standard Serial Number (ISSN)

  • 0932-5026

Electronic International Standard Serial Number (EISSN)

  • 1613-9798

abstract

  • Changes on temperature patterns, on a local scale, are perceived by individuals as the
    most direct indicators of global warming and climate change. As a specific example,
    for an Atlantic climate location, spring and fall seasons should present a mild transition
    between winter and summer, and summer and winter, respectively. By observing daily
    temperature curves along time, being each curve attached to a certain calendar day, a
    regression model for these variables (temperature curve as covariate and calendar day
    as response) would be useful for modeling their relation for a certain period. In addition,
    temperature changes could be assessed by prediction and observation comparisons
    in the long run. Such a model is presented and studied in this work, considering a
    nonparametric Nadaraya¿Watson-type estimator for functional covariate and circular
    response. The asymptotic bias and variance of this estimator, as well as its asymptotic
    distribution are derived. Its finite sample performance is evaluated in a simulation
    study and the proposal is applied to investigate a real-data set concerning temperature
    curves.

subjects

  • Statistics

keywords

  • circular data; flexible regression; functional data; temperature curves