A Comparative Study on Estimation from Regression Model for Type-I Right-Censored Data from Lognormal Distribution - Tippan, Siwaporn; Somboonsavatdee, Anupap (Journal of Applied Statistics and Information Technology, 2019)
The objective of this research is to compare the estimation methods for log-linear regression model with dependent variable under type-I right-censoring: 1) Ordinary Least Squares Method (OLS); 2) Maximum Likelihood Estimation (MLE); 3) Chatterjee and McLeish Method (CM); and 4) Maximum Likelihood Estimation using the EM algorithm(MLE_EM). The results on this research are from 81 simulated scenarios. The findings are: 1) MLE_EM and MLE perform the best at large sample size (n = 100) or high censoring proportion (r = 30%); on the other hand, 2) OLS performs the best at small sample size (n = 30) or low censoring proportion (r = 10%); and 3) CM performs the best generally for the rest of scenarios such that the sample size is moderate (n = 50) or censoring proportion is moderate (r = 20%); moreover, 4) The efficiency of all of the methods increase when sample size increase or proportion of right-censored data on dependent variable decrease or ratio of the sum of variances of two independent variables to error variance increase.