43 research outputs found
Exponentiated Extended Weibull-Power Series Class of Distributions
In this paper, we introduce a new class of distributions by compounding the
exponentiated extended Weibull family and power series family. This
distribution contains several lifetime models such as the complementary
extended Weibull-power series, generalized exponential-power series,
generalized linear failure rate-power series, exponentiated Weibull-power
series, generalized modified Weibull-power series, generalized Gompertz-power
series and exponentiated extended Weibull distributions as special cases. We
obtain several properties of this new class of distributions such as Shannon
entropy, mean residual life, hazard rate function, quantiles and moments. The
maximum likelihood estimation procedure via a EM-algorithm is presented.Comment: Accepted for publication Ciencia e Natura Journa
The Beta-Gompertz Distribution
In this paper, we introduce a new four-parameter generalized version of the
Gompertz model which is called Beta-Gompertz (BG) distribution. It includes
some well-known lifetime distributions such as beta-exponential and generalized
Gompertz distributions as special sub-models. This new distribution is quite
flexible and can be used effectively in modeling survival data and reliability
problems. It can have a decreasing, increasing, and bathtub-shaped failure rate
function depending on its parameters. Some mathematical properties of the new
distribution, such as closed-form expressions for the density, cumulative
distribution, hazard rate function, the th order moment, moment generating
function, Shannon entropy, and the quantile measure are provided. We discuss
maximum likelihood estimation of the BG parameters from one observed sample and
derive the observed Fisher's information matrix. A simulation study is
performed in order to investigate this proposed estimator for parameters. At
the end, in order to show the BG distribution flexibility, an application using
a real data set is presented.Comment: http://www.emis.de/journals/RCE/ingles/v37_1.htm
A Shift-Dependent Measure of Extended Cumulative Entropy and Its Applications in Blind Image Quality Assessment
Recently, Tahmasebi and Eskandarzadeh introduced a new extended cumulative entropy (ECE). In this paper, we present results on shift-dependent measure of ECE and its dynamic past version. These results contain stochastic order, upper and lower bounds, the symmetry property and some relationships with other reliability functions. We also discuss some properties of conditional weighted ECE under some assumptions. Finally, we propose a nonparametric estimator of this new measure and study its practical results in blind image quality assessment
Estimators for the parameter mean of Morgenstern type bivariate generalized exponential distribution using ranked set sampling
In situations where the sampling units in a study can be more easily ranked based on the measurement of an auxiliary variable, ranked set sampling provides unbiased estimators for the mean of a population that are more efficient than unbiased estimators based on simple random sampling. In this paper, we consider the Morgenstern type bivariate generalized exponential distribution and obtain several unbiased estimators for the mean parameter of its marginal distribution, based on different ranked set sampling schemes. The efficiency of all considered estimators are evaluated and several numerical illustrations are given.Peer Reviewe
Estimators for the parameter mean of Morgenstern type bivariate generalized exponential distribution using ranked set sampling
In situations where the sampling units in a study can be more easily ranked based on the measurement of an auxiliary variable, ranked set sampling provides unbiased estimators for the mean of a population that are more efficient than unbiased estimators based on simple random sampling. In this paper, we consider the Morgenstern type bivariate generalized exponential distribution and obtain several unbiased estimators for the mean parameter of its marginal distribution, based on different ranked set sampling schemes. The efficiency of all considered estimators are evaluated and several numerical illustrations are given
Estimators for the parameter mean of Morgenstern type bivariate generalized exponential distribution using ranked set sampling
In situations where the sampling units in a study can be more easily ranked based on the measurement of an auxiliary variable, ranked set sampling provides unbiased estimators for the mean of a population that are more efficient than unbiased estimators based on simple random sampling. In this paper, we consider the Morgenstern type bivariate generalized exponential distribution and obtain several unbiased estimators for the mean parameter of its marginal distribution, based on different ranked set sampling schemes. The efficiency of all considered estimators are evaluated and several numerical illustrations are given