Nadaraya-Watson Estimation of a Circular Regression Model on Peak Systolic Blood Pressure Data
DOI:
https://doi.org/10.33095/2cbwj529Abstract
Purpose: The research aims to estimate models representing phenomena that follow the logic of circular (angular) data, accounting for the 24-hour periodicity in measurement.
Theoretical framework: The regression model is developed to account for the periodic nature of the circular scale, considering the periodicity in the dependent variable y, the explanatory variables x, or both.
Design/methodology/approach: Two estimation methods were applied: a parametric model, represented by the Simple Circular Regression (SCR) model, and a nonparametric model, represented by the Nadaraya-Watson Circular Regression (NW) model. The analysis used real data from 50 patients at Al-Kindi Teaching Hospital in Baghdad.
Findings: The Mean Circular Error (MCE) criterion was used to compare the two models, leading to the conclusion that the Nadaraya-Watson (NW) circular model outperformed the parametric model in estimating the parameters of the circular regression model.
Research, Practical & Social Implications: The recommendation emphasized using the Nadaraya-Watson nonparametric smoothing method to capture the nonlinearity in the data.
Originality/value: The results indicated that the Nadaraya-Watson circular model (NW) outperformed the parametric model.
Paper type Research paper.
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References
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