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Robust Circular Logistic Regression Model and Its Application to Life and Social Sciences
Modelo de regresión logística circular robusto y su aplicación a las ciencias de la vida y sociales
DOI:
https://doi.org/10.15446/rce.v46n1.101517Keywords:
Circular data, Circular logistic regression, Maximum likelihood estimation, Multinomial circular logistic regression, Robustness (en)Datos circulares, Regresión logística circular, Regresión logística circular multinomial, Estimación de máxima verosimilitud, Robustez (es)
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This paper presents robust estimators for binary and multinomial circular logistic regression, where a circular predictor is related to the response. An extensive Monte Carlo Simulation Study clearly shows the robustness of proposed methods. Finally, three numerical examples of Botany, Crime and Meteorology illustrate the application of these methods to Life and Social Sciences. Although in the Botany data the proposed method showed little improvement, in the Crime and Meteorological data an increment up to 5\% and 4\% of accuracy, respectively, is achieved.
Este artículo presenta estimadores robustos para el modelo de regresión logística circular binomial y mutinomial. Un estudio de Monte Carlo muestra la robustez de los métodos propuestos. Finalmente, tres ejemplos numéricos en botánica, criminalística y meteorología muestran la aplicación de estos modelos a las Ciencias.
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