In this paper, we deal with hypothesis testing for unknown parameters. A new method is proposed based on a combination of the computational approach test and maximum likelihood estimation (CAT-MLE), introduced by [7]. The CAT-MLE does not require any knowledge of the sampling distribution. It depends heavily on numerical computational and Monte-Carlo simulation study. To illustrate the methodol...

We apply the techniques of stochastic integration with respect to the fractional Brownian motion and the theory of regularity and supremum estimation for stochastic processes to study the maximum likelihood estimator (MLE) for the drift parameter of stochastic processes satisfying stochastic equations driven by fractional Brownian motion with any level of Hölder-regularity (any Hurst parameter)...

This is a case study on the implementation of MLE-Moodle in museum education and in a biology exam conducted in field conditions. MLE-Moodle is an open-source GPL-licensed ready to use mobile Learning system designed for mobile phones. It is a plug-in for the open-source learning management system Moodle. MLE-Moodle is a new approach to mobile learning. It connects the mobile learning environme...

Mobile learning environments have emerged as a way to support the m-learning initiatives, providing benefits to learners, teachers and tutors. However, despite their relevance, the development of mobile learning environments present problems and challenges that must be investigated, especially with respect to the definition and adoption of architectural patterns. Motivated by this scenario, in ...

We describe an original implementation of k-Maximum Likelihood Estimator (k-MLE)[1], a fast algorithm for learning finite statistical mixtures of exponential families. Our version converges to a local maximum of the complete likelihood while guaranteeing not to have empty clusters. To initialize k-MLE, we propose a careful and greedy strategy inspired by k-means++ which selects automatically cl...

This document describes the statistically based maximum likelihood (MLE) method of estimating the values of Alpha and Beta in data sets with zero SAIDI days. Two quantitative examples show that the MLE method is more accurate than minimum value substitution, which in turn is the most accurate of the proposed substitution methods. The MLE method involves the iterative solution of a non-linear eq...

We apply the techniques of stochastic integration with respect to the fractional Brownian motion and the theory of regularity and supremum estimation for stochastic processes to study the maximum likelihood estimator (MLE) for the drift parameter of stochastic processes satisfying stochastic equations driven by fractional Brownian motion with any level of Holder-regularity (any Hurst parameter...

The Maximum Likelihood Estimator (MLE) has commonly been used to estimate the unknown parameters in the finite mixture of distributions via the expectationmaximization (EM) algorithm. However, the MLE can be very sensitive to outliers in the data. Various approaches that have incorporated robustness in fitting mixtures and clustering are discussed. Special attention is given to the Weighted Tri...

This paper introduces estimations of reliabilities index for Rayleigh components in a series system using Type-II censored and masked system life data. We derive the Maximum likelihood estimation (MLE) of the unknown parameters for Rayleigh components and reliabilities index. In view of the limitation of MLE under complete masked case, we employ the Bayes approach. Bayes estimations of the Rayl...

In this paper, we study the nonparametric maximum likelihood estimator (MLE) of a convex hazard function. We show that the MLE is consistent and converges at a local rate of n(2/5) at points x(0) where the true hazard function is positive and strictly convex. Moreover, we establish the pointwise asymptotic distribution theory of our estimator under these same assumptions. One notable feature of...