Wagner Souza - Academia.edu (original) (raw)

Papers by Wagner Souza

Computational Statistics & Data Analysis, 2010

The modeling and analysis of lifetimes is an important aspect of statistical work in a wide varie... more The modeling and analysis of lifetimes is an important aspect of statistical work in a wide variety of scientific and technological fields. For the first time, the so-called generalized exponential geometric distribution is introduced. The new distribution can have a decreasing, increasing and upside-down bathtub failure rate function depending on its parameters. It includes the exponential geometric (Adamidis and Loukas, 1998),

Journal of Statistical Computation and Simulation, 2010

We introduce the beta generalized exponential distribution that includes the beta exponential and... more We introduce the beta generalized exponential distribution that includes the beta exponential and generalized exponential distributions as special cases. We provide a comprehensive mathematical treatment of this distribution. We derive the moment generating function and the rrrth moment thus generalizing some results in the literature. Expressions for the density, moment generating function and rrrth moment of the order statistics also are obtained. We discuss estimation of the parameters by maximum likelihood and provide the information matrix. We observe in one application to real data set that this model is quite flexible and can be used quite effectively in analyzing positive data in place of the beta exponential and generalized exponential distributions.

Journal of Statistical Computation and Simulation, 2011

In this paper we introduce, for the first time, the Weibull-Geometric distribution which generali... more In this paper we introduce, for the first time, the Weibull-Geometric distribution which generalizes the exponential-geometric distribution proposed by Adamidis and Loukas (1998). The hazard function of the last distribution is monotone decreasing but the hazard function of the new distribution can take more general forms. Unlike the Weibull distribution, the proposed distribution is useful for modeling unimodal failure rates. We derive the cumulative distribution and hazard functions, the density of the order statistics and calculate expressions for its moments and for the moments of the order statistics. We give expressions for the R\'enyi and Shannon entropies. The maximum likelihood estimation procedure is discussed and an algorithm EM (Dempster et al., 1977; McLachlan and Krishnan, 1997) is provided for estimating the parameters. We obtain the information matrix and discuss inference. Applications to real data sets are given to show the flexibility and potentiality of the proposed distribution.

Computational Statistics & Data Analysis, 2011

In this paper we introduce the class Weibull power series (WPS) of distributions which is obtaine... more In this paper we introduce the class Weibull power series (WPS) of distributions which is obtained by compounding Weibull and power series distributions, where compounding procedure follows same way that was previously carried out by . This new class of distributions has as particular case the two-parameter class exponential power series (EPS) of distributions, which was introduced recently by Chahkandi and Ganjali (2009). Like EPS distributions, our class contains several distributions which have been introduced and studied such as: exponential geometric (Adamidis and Loukas, 1998), exponential Poisson and exponential logarithmic (Tahmasbi and Rezaei, 2008) distributions. Moreover, the hazard function of our class may take increasing, decreasing, upside down bathtub forms, among others, while hazard function of a EPS distribution is only decreasing. We obtain several properties of the WPS distributions such as moments, order statistics, estimation by maximum likelihood and inference for large sample. Furthermore, EM algorithm is also used to determine the maximum likelihood estimates of the parameters and we discuss maximum entropy characterizations under suitable constraints. Special distributions are studied in some details. Applications to real data sets are given to show the flexibility and potentiality of the proposed class of distributions.

Computational Statistics & Data Analysis, 2010

The modeling and analysis of lifetimes is an important aspect of statistical work in a wide varie... more The modeling and analysis of lifetimes is an important aspect of statistical work in a wide variety of scientific and technological fields. For the first time, the so-called generalized exponential geometric distribution is introduced. The new distribution can have a decreasing, increasing and upside-down bathtub failure rate function depending on its parameters. It includes the exponential geometric (Adamidis and Loukas, 1998),

Journal of Statistical Computation and Simulation, 2010

We introduce the beta generalized exponential distribution that includes the beta exponential and... more We introduce the beta generalized exponential distribution that includes the beta exponential and generalized exponential distributions as special cases. We provide a comprehensive mathematical treatment of this distribution. We derive the moment generating function and the rrrth moment thus generalizing some results in the literature. Expressions for the density, moment generating function and rrrth moment of the order statistics also are obtained. We discuss estimation of the parameters by maximum likelihood and provide the information matrix. We observe in one application to real data set that this model is quite flexible and can be used quite effectively in analyzing positive data in place of the beta exponential and generalized exponential distributions.

Journal of Statistical Computation and Simulation, 2011

In this paper we introduce, for the first time, the Weibull-Geometric distribution which generali... more In this paper we introduce, for the first time, the Weibull-Geometric distribution which generalizes the exponential-geometric distribution proposed by Adamidis and Loukas (1998). The hazard function of the last distribution is monotone decreasing but the hazard function of the new distribution can take more general forms. Unlike the Weibull distribution, the proposed distribution is useful for modeling unimodal failure rates. We derive the cumulative distribution and hazard functions, the density of the order statistics and calculate expressions for its moments and for the moments of the order statistics. We give expressions for the R\'enyi and Shannon entropies. The maximum likelihood estimation procedure is discussed and an algorithm EM (Dempster et al., 1977; McLachlan and Krishnan, 1997) is provided for estimating the parameters. We obtain the information matrix and discuss inference. Applications to real data sets are given to show the flexibility and potentiality of the proposed distribution.

Computational Statistics & Data Analysis, 2011

In this paper we introduce the class Weibull power series (WPS) of distributions which is obtaine... more In this paper we introduce the class Weibull power series (WPS) of distributions which is obtained by compounding Weibull and power series distributions, where compounding procedure follows same way that was previously carried out by . This new class of distributions has as particular case the two-parameter class exponential power series (EPS) of distributions, which was introduced recently by Chahkandi and Ganjali (2009). Like EPS distributions, our class contains several distributions which have been introduced and studied such as: exponential geometric (Adamidis and Loukas, 1998), exponential Poisson and exponential logarithmic (Tahmasbi and Rezaei, 2008) distributions. Moreover, the hazard function of our class may take increasing, decreasing, upside down bathtub forms, among others, while hazard function of a EPS distribution is only decreasing. We obtain several properties of the WPS distributions such as moments, order statistics, estimation by maximum likelihood and inference for large sample. Furthermore, EM algorithm is also used to determine the maximum likelihood estimates of the parameters and we discuss maximum entropy characterizations under suitable constraints. Special distributions are studied in some details. Applications to real data sets are given to show the flexibility and potentiality of the proposed class of distributions.