Parameter Estimation of Spatial Error Model – Multivariate Adaptive Generalized Poisson Regression Spline

Septia Devi Prihastuti Yasmirullah, Bambang Widjanarko Otok*, Jerry Dwi Trijoyo Purnomo, Dedy Dwi Prastyo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The non-parametric regression method becomes an alternative that prioritizes flexibility. Therefore, it is possible to obtain a regression curve model when its shape is not yet known. Multivariate adaptive regression spline (MARS) is one of the non-parametric approaches. In 1991, MARS was introduced by Friedman. The MARS approach, which uses nonparametric regression, can consider additive and interactive effects between predictor variables. MARS modeling has typically been used to model continuous or categorical data. However, researchers in the health sector not only encounter data with continuous or categorical responses but also count data. The original MARS method did not support count data with varying variances and means. Therefore, this study aims to develop the Spatial Error Model—Multivariate Adaptive Generalized Poisson Regression Spline (SEM-MAGPRS), which combines the MARS method with the generalized Poisson regression method with spatial effects.

Original languageEnglish
Pages (from-to)1265-1272
Number of pages8
JournalEngineering Letters
Volume31
Issue number3
Publication statusPublished - 1 Aug 2023

Keywords

  • MARS
  • SEM
  • count data
  • generalized Poisson regression
  • spatial regression

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