Penaksiran Parameter Regresi Poisson dengan Maximum Likelihood

Abstract

Poisson regression is a regression analysis employed to model a response variable (Y) with a predictor variable (X). This research aims to estimate the parameters in the poisson regression model using the Maximum Likelihood Estimation (MLE) method. The selection of the Maximum Likelihood Estimation (MLE) method is motivated by its capability to provide consistent and efficient estimates in modeling the relationship between predictor variables and response variables in poisson regression. The Maximum Likelihood Estimation (MLE) equation is completed using the Fisher Scoring algorithm, an iterative approach that is utilized to update parameters repeatedly to approach the maximum likelihood value. This research was applied to the number of accident data on Minnesota state highways, and the obtained poisson regression model is represented by Y1 = exp(5,7223 – 0,0217xi1 – 0,4294xi2 – 0,0428xi3 – 0,1112xi ̂ .