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This study aims to compare the effectiveness of functional linear regression and interaction models in predicting rainfall in Indonesia.

Comparative Study of Functional Linear Regression and an Interaction Model for Modeling Rainfall in Indonesia

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Comparative analysis reveals functional linear regression models rainfall in Indonesia, suggesting improved data fitting.

This study aims to compare the effectiveness of functional linear regression and interaction models in predicting rainfall in Indonesia.
Utilized functional prediction regression to analyze climate data from 1951 to 2016.
Compared two statistical models: functional linear effect and linear interaction combination.
Employed boosting techniques and the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) for estimation.
Functional linear model provided a better fit for rainfall data compared to the interaction model.
Achieved a smaller Akaike's Information Criterion value of 170.351, indicating improved model performance.

Khikmah et al. (2026) studied this question.

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