METODE RIDGE REGRESSION DALAM KASUS MULTIKOLINEARITAS PADA REGRESI POISSON; (RIDGE REGRESSION METHOD IN CASES OF MULTICOLLINEARITY ON POISSON REGRESSION)

Widagdo, Ganang, Sri Haryatmi

2015 | Skripsi | FMIPA

Poisson Regression is one of the statistical methods that used to perform model relationship between response variable which has poisson distribution with discrete scale and prediktor variables. There are some assumptions should be found in classical regression analysis using Least Square Method, one of them is no multicollinearity. If this assumption is not met, parameter estimation using Least Square method become less valid and the variance of error will be large. And now, there are a lot of variety methods for solving this multicollinearity problem, one of them is ridge regression. The concept of ridge is by adding a ridge parameter value of k which is diagonal matrix, to the correlation matrix . poisson Regression Model is usually estimated by using maximum likelihood (ML) method but its unstable to multicollinearity. Therefore, we present a new poisson ridge regression estimator as a remedy to the problem of instability of the traditional ML method. To investigate the performance of the Poisson Ridge Regression and the traditional Maximum Likelihood approaches for estimating the parameters of the poisson regression model, we calculate the mean squared error (MSE) as the comparison. The result from this calculation shows that Poisson Ridge Regression method is better than the traditional ML estimator. In this paper we will discuss some ridge parameters of k for poisson regression model. The ridge parameter which could producing the smallest value of Mean Square Error will be diagonally added in correlation matrix on Poisson Ridge Regression estimator. Finally, we get a Poisson Regression Model with biased estimators and has low variance.

Kata Kunci : Poisson Regression; Maximum Likelihood,; Ridge; Regression; Mean Square Error; Multicollinearity.