Comparison of multivariate adaptive Poisson Regression spline and multivariate adaptive generalized Poisson Regression spline

The multivariate adaptive regression (MARS) spline is a statistical method introduced by Friedman in 1991. The MARS method has several advantages, especially for data that consider the potential nonlinear pattern. These produce a continuous model on knots where the knot selection process is performe...

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Bibliographic Details
Main Authors: Yasmirullah, Septia Devi Prihastuti, Otok, Bambang Widjanarko, Purnomo, Jerry Dwi Trijoyo, Prastyo, Dedy Dwi
Format: Conference Proceeding
Language:English
Subjects:
Knots
Multivariate analysis
Poisson density functions
Regression models
Statistical analysis
Statistical methods
Tuberculosis
Online Access:Get full text
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Summary:The multivariate adaptive regression (MARS) spline is a statistical method introduced by Friedman in 1991. The MARS method has several advantages, especially for data that consider the potential nonlinear pattern. These produce a continuous model on knots where the knot selection process is performed via adaptive procedures, capturing additive effects and interactions between predictor variables, and applied to data modeling with numerical and categorical responses. However, the analyzed data is not only numerical or categorical data, but also count type data. Count type data is often found in the health sector, for example, the number of data on tuberculosis. It invites to modify the MARS model with Poisson regression and generalized Poisson regression, i.e. Multivariate Adaptive Poisson Regression Spline (MAPRS) and Multivariate Adaptive Generalized Poisson Regression Spline (MAGPRS) . In addition, this study aimed to compare MAPRS and MAGPRS when applied to tuberculosis data. By applying MAPRS and MAGPRS to model the number of tuberculosis in Lamongan regency, the MAPRS method outperforms the MAGPRS method. The MAPRS method has a lower RMSE value than the MAGPRS method.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0119385