Computational Statistics & Data Analysis
A two-parameter lifetime distribution with decreasing failure rate
Computational Statistics & Data Analysis
On some lifetime distributions with decreasing failure rate
Computational Statistics & Data Analysis
A new distribution with decreasing, increasing and upside-down bathtub failure rate
Computational Statistics & Data Analysis
Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy
IEEE Transactions on Information Theory
Bivariate gamma-geometric law and its induced Lévy process
Journal of Multivariate Analysis
Generalized exponential-power series distributions
Computational Statistics & Data Analysis
The compound class of extended Weibull power series distributions
Computational Statistics & Data Analysis
Original article: Exponentiated Weibull-Poisson distribution: Model, properties and applications
Mathematics and Computers in Simulation
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In this paper we introduce the Weibull power series (WPS) class of distributions which is obtained by compounding Weibull and power series distributions, where the compounding procedure follows same way that was previously carried out by Adamidis and Loukas (1998). This new class of distributions has as a particular case the two-parameter exponential power series (EPS) class of distributions (Chahkandi and Ganjali, 2009), which contains several lifetime models such as: exponential geometric (Adamidis and Loukas, 1998), exponential Poisson (Kus, 2007) and exponential logarithmic (Tahmasbi and Rezaei, 2008) distributions. The hazard function of our class can be increasing, decreasing and upside down bathtub shaped, among others, while the hazard function of an 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 a large sample. Furthermore, the 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 detail. Applications to two real data sets are given to show the flexibility and potentiality of the new class of distributions.