A class of regression models for parallel and series systems with a random number of components. Issue 2 (4th March 2017)

• Record Type: Journal Article
• Authors: Morais, Alice L.; Ferrari, Silvia L. P.
• Is Part Of: Statistics. Volume 51:Issue 2(2017)
• Page Start: 294
• Page End: 313
• Publication Date: 2017-03-04
• Subjects: Weibull distribution -- quantile inference -- regression model -- systems with random number of components; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2016.1268613
• Languages: English
• ISSNs: 0233-1888

Abstract

ABSTRACT: We extend the Weibull power series (WPS) class of distributions to the new class of extended Weibull power series (EWPS) class of distributions. The EWPS distributions are related to series and parallel systems with a random number of components, whereas the WPS distributions [Morais AL, Barreto-Souza W. A compound class of Weibull and power series distributions. Computational Statistics and Data Analysis . 2011;55:1410–1425] are related to series systems only. Unlike the WPS distributions, for which the Weibull is a limiting special case, the Weibull law is a particular case of the EWPS distributions. We prove that the distributions in this class are identifiable under a simple assumption. We also prove stochastic and hazard rate order results and highlight that the shapes of the EWPS distributions are markedly more flexible than the shapes of the WPS distributions. We define a regression model for the EWPS response random variable to model a scale parameter and its quantiles. We present the maximum likelihood estimator and prove its consistency and asymptotic normal distribution. Although series and parallel systems motivated the construction of this class, the EWPS distributions are suitable for modelling a wide range of positive data sets. To illustrate potential uses of this model, we apply it to a real data set on the tensile strength of coconut fibres and present a simple device for diagnostic purposes.