A comparison of the method of moments estimator and maximum likelihood estimator for the success probability in the Fibonacci-type probability distribution

A Fibonacci-type probability distribution provides the probabilistic models for establishing stopping rules associated with the number of consecutive successes. It can be interpreted as a generalized version of a geometric distribution. In this article, after revisiting the Fibonacci-type probability distribution to explore its definition, moments and properties, we proposed numerical methods to obtain two estimators of the success probability: the method of moments estimator (MME) and maximum likelihood estimator (MLE). The ways both of them performed were compared in terms of the mean squared error. A numerical study demon-srated that the MLE tends to outperform the MME for most of the parameter space with various sample sizes.