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 $k$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

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