Modification of Multivariate Adaptive Regression Spline (MARS)

Abstract Multivariate Adaptive Regression Spline (MARS) is a nonparametric regression method that can accommodate additive effects and interaction effects between predictor variables. Generally, MARS has been used for modeling pairs of data with continuous or categorical responses. One type of categ...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1863; no. 1; p. 12078
Main Authors Prihastuti Yasmirullah, Septia Devi, Otok, Bambang Widjanarko, Trijoyo Purnomo, Jerry Dwi, Prastyo, Dedy Dwi
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.03.2021
Subjects
Model testing
Modelling
Multivariate analysis
Physics
Poisson density functions
Regression
Tuberculosis
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Summary:Abstract Multivariate Adaptive Regression Spline (MARS) is a nonparametric regression method that can accommodate additive effects and interaction effects between predictor variables. Generally, MARS has been used for modeling pairs of data with continuous or categorical responses. One type of categorical data that needs special attention in modeling is count data. The count data is often encountered, especially in the health sector. The existence of count data motivates the development of the theory and application of the MARS method, which is the Multivariate Adaptive Poisson Regression Spline (MAPRS). The MAPRS is a combination of MARS and Poisson regression. It can accommodate and analyze the data according to its type and distribution. The application of MAPRS to model the count of Tuberculosis (TB) shows that it outperforms the Poisson regression.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1863/1/012078