The aim of this study was to describe some parametric estimation methods for the Weibull, gamma and Gompertz distributions and to identify among them estimators the most efficient in practical applications. Techniques which are considered as traditional methods, like the maximum likelihood (MLE) and the method of moments (MM) estimation but also some newer and less commonly used techniques like the Lmoment estimator (LME), least-square estimator (LSE), generalized spacing estimator (GSE) and percentile estimator (PE) were presented. The application of each method was demonstrated in a simulation study using data sets generated for different distribution parameters and sample sizes. Discussed estimators were compared in terms of their efficiency and bias measured by mean-square errors (MSE) based on the simulations results.

The aim of this study was to describe some parametric estimation methods for the Weibull, gamma and Gompertz distributions and to identify among them estimators the most efficient in practical applications. Techniques which are considered as traditional methods, like the maximum likelihood (MLE) and the method of moments (MM) estimation but also some newer and less commonly used techniques like the Lmoment estimator (LME), least-square estimator (LSE), generalized spacing estimator (GSE) and percentile estimator (PE) were presented. The application of each method was demonstrated in a simulation study using data sets generated for different distribution parameters and sample sizes. Discussed estimators were compared in terms of their efficiency and bias measured by mean-square errors (MSE) based on the simulations results.