REGRESI LOGISTIK ROBUST DENGAN ESTIMATOR BIANCO-YOHAI; ROBUST LOGISTIC REGRESSION WITH BIANCO-YOHAI ESTIMATOR

Putra, Aga Aswanta, Subanar

2016 | Skripsi | FMIPA

We often find data with qualitative response variable with binary categories in some researches especially health study. Binary logistic regression usually used to analyze that researches. A method to estimate parameters in this regression is Maximum Likelihood Estimation (MLE) with Newton Raphson or Fisher Scoring algorithm. The estimations from MLE is less accurate if there are some outliers in data sample. Therefore, we can use Bianco-Yohai estimator to solve this problem. We use this estimator to analyze the influences of parameters that can make someone affected by prostate cancer. Then, the result of this estimation will be compared with the result of binary logistic regression (MLE). From the MSE and R2 value, we can conclude that the Bianco-Yohai estimator is more robust to outlier than binary logistic regression with MLE.

Kata Kunci : binary logistic regression, maximum likelihood estimator, outlier, robust regression; Bianco-Yohai estimator; Fisher Scoring algorithm; Newton Raphson algorithm; Prostate Cancer Study