The Zeta-G Class: Some Computational and Analytical Aspects

Authors

  • Ana Carla Percontini UEFS
  • Frank Gomes-Silva UFRPE
  • Gauss Moutinho Cordeiro UFPE
  • Pedro Rafael Diniz Marinho Universidade Federal da Paraíba

DOI:

https://doi.org/10.5902/2179460X39914

Abstract

We define a new class of distributions with one extra shape
parameter including some special cases. We provide numerical and computational aspects of the new class. We propose
functions using the \textsf{R} language to fit any distribution in this family to a data set. In addition, such functions are implemented efficiently
using the library \textsf{Rcpp} that enables the incorporation of the codes \textsf{C++} in \textsf{R} automatically. Some examples are presented
for using the implemented routines in practice. We derive some mathematical properties of this class including explicit expressions
for the moments, generating function and mean deviations. We discuss the estimation of the model parameters
by maximum likelihood and provide an application to a real data set.

Downloads

Download data is not yet available.

Author Biography

Pedro Rafael Diniz Marinho, Universidade Federal da Paraíba

Possui bacharelado em estatística pela Universidade Federal da Paraíba - UFPB (2010), mestrado em estatística pela Universidade Federal de Pernambuco - UFPE (2014) e doutorado em estatística pela UFPE (2016). Possui interesses em estatística computacional, construção de aplicações para estatística, inferência estatística e probabilidade. Atualmente é professor Adjunto do Departamento de Estatística da Universidade Federal da Paraíba - UFPB e membro permanente do Programa de Pós-graduação em Modelagem Matemática e Computacional - PPGMMC vinculado ao Centro de Informática da UFPB.

References

Akaike, H. (1981). This week’s citation classic. Current Contents Engineering, Technology, and Applied Sciences, 12, 42.

Alzaatreh, A., Lee, C., Famoye, F. (2013). A new method for generating families of continuous distributions. Metron, 71, 63–79, URL http://dx.doi.org/10.1007/s40300-013-0007-y.

Barlow, R. E., Davis, B. (1977). Analysis of time between failures for repairable components. Relatório Técnico, California Uni Berkekey Operations Research Center.

Chen, G., Balakrishnan, N. (1995). A general purpose approximate goodness-of-fit test. Journal of Quality Technology, 27(2), 154–161.

Cordeiro, G. M., Lemonte, A. J. (2013). On the Marshall-Olkin extended Weibull distribution. Statistical Papers, 54, 333–353, URL http://dx.doi.org/10.1007/s00362-012-0431-8.

Diniz Marinho, P. R., Bourguignon, M., Barros Dias, C. R. (2016). AdequacyModel: Adequacy of Probabilistic Models and General Purpose Optimization. URL https://CRAN.R-project.org/package=AdequacyModel, r package version 2.0.0.

Doray, L. G., Luong, A. (1995). Quadratic distance estimators for the Zeta family. Insurance: Mathematics and Economics, 16, 255–260.

Doray, L. G., Luong, A. (1997). Efficient estimators for the good family. Communications in Statistics-Simulation and Computation, 26, 1075–1088.

Gupta, P. L., Gupta, R. C., Ong, S. H., Srivastava, H. M. (2008). A class of hurwitz–lerch zeta distributions and their applications in reliability. Applied Mathematics and Computation, 196, 521–531.

Gut, A. (2005). Probability: A Graduate Course. Springer Verlag, New York.

Gut, A. (2006). Some remarks on the Riemann Zeta distribution. Revue Roumaine de Mathematiques Pures et Appliquees, 51, 205–217.

Hannan, E. J., Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society: Series B (Methodological), 41(2), 190–195.

Hurvich, C. M., Tsai, C. L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297–307.

K. Oldham, J. M., Spainer, J. (2009). An Atlas of Functions. Springer.

Kundu, D., Gupta, R. D. (1999). Generalized exponential distribution. The Australian and New Zealand Journal of Statistics, 41, 173–188.

Lin, G. D., Hu, C. Y. (2001). The Riemann Zeta distribution. Bernoulli, 7, 817–828.

Nadarajah, S., Gupta, A. K. (2007). The exponentiated gamma distribution with application to drought data. Bulletin of the Calcutta Statistical Association, 59, 29–54.

Nadarajah, S., Kotz, S. (2006). The exponentiated type distributions. Acta Applicandae Mathematica, 92, 97–111, URL http://dx.doi.org/10.1007/s10440-006-9055-0.

Nadarajah, S., Cordeiro, G. M., Ortega, E. M. M. (2013). The exponentiated Weibull distribution: a survey. Statistical Papers, 54, 839–877, URL http://dx.doi.org/10.1007/s00362-012-0466-x.

Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464.

Suprawhardana, M. S., Prayoto, S. (1999). Total time on test plot analysis for mechanical components of the rsg-gas reactor. Atom Indonesia, 25(2), 155–161.

Vilaplana, J. P. (1988). The Hurwitz distribution. in: M. L. Puri, J. P. Vilaplana, W. Wertz (Eds.), New Perspectives in Theoretical and Applied Statistics (Bilbao, 1986), Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York.

Zörnig, P., Altmann, G. (1995). Unified representation of Zipf distributions. Computational Statistics & Data Analysis, 19, 461–473.

Downloads

Published

2020-12-23

How to Cite

Percontini, A. C., Gomes-Silva, F., Cordeiro, G. M., & Marinho, P. R. D. (2020). The Zeta-G Class: Some Computational and Analytical Aspects. Ciência E Natura, 42, e111. https://doi.org/10.5902/2179460X39914