The measure strength of predictive in the poisson regression model with regression correlation coefficient case study of maternal mortality rate in Central Java Province in 2015

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Abstract

RCC which stands for regression correlation coefficient is an alternative measure strength of predictive that can be applied to the GLM model in which the distribution of response variable is not only normal. The RCC is constructed based on the definition of correlation coefficient by using generalized linear model (GLM). So, the RCC can be defined as a value that states the strength of the relationship between response variable Y and its conditional expectation given predictor variables E[Y|X]. The RCC is one measure of predictable power that can satisfies the property like applicability, interpretability, consistency, and affinity. In general, the explicit form of RCC on GLM is difficult to find. However, when RCC is applied to the Poisson regression model and the predictor variables are assumed to be a normal multivariate distribution, an explicit form is found. This explicit form still contains the unknown parameters derived from the Poisson regression model. Therefore, we need to find an estimate of these parameters to obtain an estimator from the RCC. The Poisson regression model which still contains the unknown parameters are estimated using maximum likelihood method. Application of regression correlation coefficient is done in case of maternal mortality rate in Central Java Province in 2015.

Original languageEnglish
Article number012090
JournalJournal of Physics: Conference Series
Volume1725
Issue number1
DOIs
Publication statusPublished - 12 Jan 2021
Event2nd Basic and Applied Sciences Interdisciplinary Conference 2018, BASIC 2018 - Depok, Indonesia
Duration: 3 Aug 20184 Aug 2018

Keywords

  • Maternal mortality rate
  • Measure strength of predictive
  • Poisson regression model
  • Regression correlation coefficient

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