10,927 research outputs found
On Bivariate Exponentiated Extended Weibull Family of Distributions
In this paper, we introduce a new class of bivariate distributions called the
bivariate exponentiated extended Weibull distributions. The model introduced
here is of Marshall-Olkin type. This new class of bivariate distributions
contains several bivariate lifetime models. Some mathematical properties of the
new class of distributions are studied. We provide the joint and conditional
density functions, the joint cumulative distribution function and the joint
survival function. Special bivariate distributions are investigated in some
detail. The maximum likelihood estimators are obtained using the EM algorithm.
We illustrate the usefulness of the new class by means of application to two
real data sets.Comment: arXiv admin note: text overlap with arXiv:1501.03528 by other author
Inference on P(Y<X) in Bivariate Rayleigh Distribution
This paper deals with the estimation of reliability when is a
random strength of a component subjected to a random stress and
follows a bivariate Rayleigh distribution. The maximum likelihood estimator of
and its asymptotic distribution are obtained. An asymptotic confidence
interval of is constructed using the asymptotic distribution. Also, two
confidence intervals are proposed based on Bootstrap method and a computational
approach. Testing of the reliability based on asymptotic distribution of is
discussed. Simulation study to investigate performance of the confidence
intervals and tests has been carried out. Also, a numerical example is given to
illustrate the proposed approaches.Comment: Accepted for publication. Communications in Statistics- Theory and
Methods, 201
Generalized Marshall-Olkin Distributions, and Related Bivariate Aging Properties
National Natural Science Foundation of China [10771090]A class of generalized bivariate Marshall-Olkin distributions, which includes as special cases the Marshall-Olkin bivariate exponential distribution and the Marshall-Olkin type distribution due to Muliere and Scarsini (1987) [19] are examined in this paper. Stochastic comparison results are derived, and bivariate aging properties, together with properties related to evolution of dependence along time, are investigated for this class of distributions. Extensions of results previously presented in the literature are provided as well. (C) 2011 Elsevier Inc. All rights reserved
On the entropy flows to disorder
Gamma distributions, which contain the exponential as a special case, have a
distinguished place in the representation of near-Poisson randomness for
statistical processes; typically, they represent distributions of spacings
between events or voids among objects. Here we look at the properties of the
Shannon entropy function and calculate its corresponding flow curves. We
consider univariate and bivariate gamma, as well as Weibull distributions which
also include exponential distributions.Comment: Enlarged version of original. 11 pages, 6 figures, 15 reference
Classical and Bayesian Inference of a Mixture of Bivariate Exponentiated Exponential Model
Exponentiated exponential (EE) model has been used effectively in reliability, engineering, biomedical, social sciences, and other applications. In this study, we introduce a new bivariate mixture EE model with two parameters assuming two cases, independent and dependent random variables. We develop a bivariate mixture starting from two EE models assuming two cases, two independent and two dependent EE models. We study some useful statistical properties of this distribution, such as marginals and conditional distributions and product moments and conditional moments. In addition, we study a dependent case, a new mixture of the bivariate model based on EE distribution marginal with two parameters and with a bivariate Gaussian copula. Different methods of estimation for the model parameters are used both under the classical and under the Bayesian paradigm. Some simulation studies are presented to verify the performance of the estimation methods of the proposed model. To illustrate the flexibility of the proposed model, a real dataset is reanalyzed
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