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.

Original languageEnglish
Article number012078
JournalJournal of Physics: Conference Series
Volume1863
Issue number1
DOIs
Publication statusPublished - 19 Apr 2021
EventInternational Conference on Mathematics, Statistics and Data Science 2020, ICMSDS 2020 - Bogor, Indonesia
Duration: 11 Nov 202012 Nov 2020

Fingerprint

Dive into the research topics of 'Modification of Multivariate Adaptive Regression Spline (MARS)'. Together they form a unique fingerprint.

Cite this