![]() ![]() The ways both of them performed were compared in terms of the mean squared error. 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). 1 List of parametric models Bernoulli distribution Ber(p): X 1 with probability p, and X 0 with probability q 1 p, p, 2 pq. maximum likelihood method - optimal for large samples. method of moments - simple, can be used as a rst approximation for the other method, 2. Let X1, X2, X3, X4, and X5 be a random sample from a binomial distribution with n 10 and p unknown. ![]() It can be interpreted as a generalized version of a geometric distribution. Two basic methods of nding good estimates 1. Xn constitute a random sample of size n from a geometric population, find formulas for estimating its parameter theta by using: (a) the method of moments. 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 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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