On parameter estimation for the generalized gamma distribution based on left‐truncated and right‐censored data. Issue 1 (3rd April 2020) - Shang, Xiangwen; Ng, Hon Keung Tony
Abstract: In this paper, we discuss the parameter estimation for the generalized gamma distribution based on left‐truncated and right‐censored data. A stochastic version of the expectation‐maximization (EM) algorithm is proposed as an alternative method to compute approximate maximum likelihood estimates. Two different methods to obtain reliable initial estimates of the parameters required for the iterative algorithms are also proposed. Interval estimation based on a parametric bootstrap method is discussed. The proposed methodologies are illustrated with a numerical example. Then, a Monte Carlo simulation study is used to evaluate the performance of the proposed estimation procedures and to compare with the direct optimization method and the conventional EM algorithm. Based on the simulation results, we show that the proposed stochastic EM algorithm is a useful alternative estimation method for the model fitting of the generalized gamma distribution.