Analisis Regresi Logistik Biner dengan Metode Penalized Maximum Likelihood pada Penyakit Covid-19 di RSUD Pringsewu - Lutfi Anisa; Nur Azizah Komara Rifai (UPT Publikasi Ilmiah Unisba, 2022)
Abstract. Parameter estimation from binary logistic regression models is usually solved using the maximum likelihood estimation (MLE) method which is then completed by the Newton-Raphson iteration method. However, in certain circumstances the MLE method cannot be used if there is one or a combination of several explanatory variables and the response is a separation case problem that causes the estimators to not converge. To overcome this problem, the penalized maximum likelihood estimation (PMLE). The results of the analysis of this study indicate that the Covid-19 patient data at the Pringsewu Hospital contains a quasi-complete separation, so the PMLE method approach is used. The estimation of the best model parameters for this data contains factors of gender, age, congenital disease (comorbid), and symptoms of shortness of breath that significantly affect the mortality rate due to Covid-19. From the results of the analysis of this study when compared with previous studies Sholihin, et al. In the journal entitled