In this thesis, classical and Bayesian estimators have been discussed for two- parameter Exponential-Logarithmic distribution based on type-I hybrid and progressive type-II censored samples. Maximum Likelihood Estimators (MLEs) in the traditional tech- nique are noted to lack closed form expressions. To compute the MLEs, we suggest using both the EM and SEM techniques. The asymptotic confidence intervals are built using the observed Fisher information matrix and the missing information principle. We develop the Bayes estimators using the Bayesian method in relation to various symmetric and asymmetric loss functions. We employ Tierney-Kadane and the significance sampling approaches in this regard. For illustrative purposes, Monte-Carlo simulation and a few real data set examples have been provided.
