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Generalized Birnbaum‐Saunders distributions applied to air pollutant concentration
Author(s) -
Leiva Víctor,
Barros Michelli,
Paula Gilberto A.,
Sanhueza Antonio
Publication year - 2008
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.861
Subject(s) - estimator , log normal distribution , goodness of fit , econometrics , method of moments (probability theory) , air pollution , distribution (mathematics) , inference , generalized method of moments , statistics , mathematics , computer science , mathematical analysis , chemistry , organic chemistry , artificial intelligence
The generalized Birnbaum‐Saunders (GBS) distribution is a new class of positively skewed models with lighter and heavier tails than the traditional Birnbaum‐Saunders (BS) distribution, which is largely applied to study lifetimes. However, the theoretical argument and the interesting properties of the GBS model have made its application possible beyond the lifetime analysis. The aim of this paper is to present the GBS distribution as a useful model for describing pollution data and deriving its positive and negative moments. Based on these moments, we develop estimation and goodness‐of‐fit methods. Also, some properties of the proposed estimators useful for developing asymptotic inference are presented. Finally, an application with real data from Environmental Sciences is given to illustrate the methodology developed. This example shows that the empirical fit of the GBS distribution to the data is very good. Thus, the GBS model is appropriate for describing air pollutant concentration data, which produces better results than the lognormal model when the administrative target is determined for abating air pollution. Copyright © 2007 John Wiley & Sons, Ltd.