The ways both of them performed were compared in terms of the mean squared error. Xi i 1 2 ::: n are iid exponential, with pdf f(x ) e xI(x > 0) The rst moment is then 1( ) 1. Example : Method of Moments for Exponential Distribution. In this article, after revisiting the Fibonaccitype 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). normal distribution) for a continuous and dierentiable function of a sequence of r.v.s that already has a normal limit in distribution. It can be interpreted as a generalized version of a geometric distribution. Summary/Abstract: A Fibonacci-type probability distribution provides the probabilistic models for establishing stopping rules associated with the number of consecutive successes. Published by: Główny Urząd Statystyczny Keywords: Fibonacci probability distribution generalized polynacci distribution factorial moment generating function method of moments maximum likelihood estimator begingroup On your final point, try some data such as 0,50,100,101,112,113,114,115,150,225 to give method of moments estimates of 12 and 204, which are clearly not wide enough endgroup Henry. A comparison of the method of moments estimator and maximum likelihood estimator for the success probability in the Fibonacci-type probability distributionĪ comparison of the method of moments estimator and maximum likelihood estimator for the success probability in the Fibonacci-type probability distribution Author(s): Yeil Kwon
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