Weighted Means, Strict Ergodicity, and Uniform Distributions

A Note on Noncommutative Unique Ergodicity and Weighted Means

converge to ET (x) in A for any x ∈ A, as n → ∞, here ET is an projection of A to the fixed point subspace of T . Note that when T is a completely positive mapping then ET is a conditional expectation. We provide an example of uniquely ergodic dynamical system relative its fixed point subspace, for which ET is not a conditional expectation. It is also constructed a uniquely ergodic entangled Ma...

Using Weighted Distributions for Modeling‎ Skewed‎, ‎Multimodal and Truncated Data‎

When the observations reflect a multimodal‎, ‎asymmetric or truncated construction or a combination of them‎, ‎using usual unimodal and symmetric distributions leads to misleading results‎. ‎Therefore‎, ‎distributions with ability of modeling skewness‎, ‎multimodality and truncation have been in the core of interest in statistical literature‎, ‎always‎. ‎There are different methods to contract ...

The Weighted Exponentiated Family of Distributions: Properties, Applications and Characterizations

In this paper a new method of introducing an additional parameter to a continuous distribution is proposed, which leads to a new class of distributions, called the weighted exponentiated family. A special sub-model is discussed. General expressions for some of the mathematical properties of this class such as the moments, quantile function, generating function and order statistics are derived;...

Bilateral Weighted Fuzzy C-Means Clustering

Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some k...

Weighted distributions