A Comparative Study Between Two Discrete Lindley Distributions

Authors

  • Ricardo Puziol Oliveira Department of Statistics, Maringá State University, PR, Brazil
  • Josmar Mazucheli Department of Statistics, Maringá State University, PR, Brazil
  • Jorge Alberto Achcar Department of Social Medicine, University of São Paulo, SP, Brazil

DOI:

https://doi.org/10.5902/2179460X25186

Keywords:

Discretization methods, Lindley distribution, likelihood, series, survival analysis, Monte Carlo simulation

Abstract

The methods of generate a probability function from a probability density function has long been used in recent years. In general, the discretization process produces probability functions that can be rivals to traditional distributions used in the analysis of count data as the geometric, the Poisson and negative binomial distributions. In this paper, by the method based on an infinite series, we studied an alternative discrete Lindley distribution to those study in Gomez (2011) and Bakouch (2014). For both distributions, a simulation study is carried out to examine the bias and mean squared error of the maximum likelihood estimators of the parameters as well as the coverage probability and the width of the confidence intervals. For the discrete Lindley distribution obtained by infinite series method we present the analytical expression for bias reduction of the maximum likelihood estimator. Some examples using real data from the literature show the potential of these distributions. 

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References

Aghababaei Jazi, M., Lai, C. D., Hossein Alamatsaz, M. (2010). A discrete inverse Weibull distribution and estimation

of its parameters. Statistical Methodology, 7, 121–132.

Bakouch, H. S., Jazi, M. A., Nadarajah, S. (2014). A new discrete distribution. Statistics, 48 (1), 200–240.

Bi, Z., Faloutsos, C., Korn, F. (2001). The DGX distribution for mining massive, skewed data. Em: Proceedings of the

seventh ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp. 17–26.

Bracquemond, C., Gaudoin, O. (2003). A survey on discrete lifetime distributions. International Journal of Reliability,

Quality and Safety Engineering, 10 (01), 69–98.

Chakraborty, S. (2015). Generating discrete analogues of continuous probability distributions - a survey of methods

and constructions. Journal of Statistical Distributions and Applications, 2 (1), 1–30.

Chakraborty, S., Chakravarty, D. (2012). Discrete gamma distributions: properties and parameter estimations.

Communications in Statistics-Theory and Methods, 41 (18), 3301–3324.

Collett, D. (2003). Modelling Survival Data in Medical Research, 2o edn. Chapaman and Hall, New York.

Cox, D. R., Snell, E. J. (1968). A general definition of residuals. Journal of the Royal Statistical Society Series B

(Methodological), 30 (2), 248–275.

Deb, P., Trivedi, P. K., et al. (1997). Demand for medical care by the elderly: a finite mixture approach. Journal of

applied Econometrics, 12 (3), 313–336.

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

Computation, 26 (3), 1075–1088.

Gómez-Déniz, E., Calderı́n-Ojeda, E. (2011). The discrete Lindley distribution: properties and applications. Journal of

Statistical Computation and Simulation, 81 (11), 1405–1416.

Good, I. J. (1953). The population frequencies of species and the estimation of population parameters. Biometrika,

(3-4), 237–264.

Haight, F. A. (1957). Queueing with balking. Biometrika, 44 (3/4), 360–369.

Hamada, M. S., Wilson, A. G., Reese, C. S., Martz, H. F. (2008). Bayesian reliability. Springer Series in Statistics,

Springer, New York.

Hussain, T., Ahmad, M. (2014). Discrete inverse Rayleigh distribution. Pak J Statist, 30 (2), 203–222.

Inusah, S., J. Kozubowski, T. (2006). A discrete analogue of the Laplace distribution. Journal of Statistical Planning

and Inference, 136.

Kalbfleisch, J. D., Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data, 2o edn. Wiley, New York, NY.

Keilson, J., Gerber, H. (1971). Some results for discrete unimodality. Journal of the American Statistical Association,

Kemp, A. W. (1997). Characterizations of a discrete normal distribution. Journal of Statistical Planning and Inference,

(2), 223 – 229, in Honor of C.R. Rao.

Kemp, A. W. (2004). Classes of discrete lifetime distributions.

Kemp, A. W. (2008). The discrete half-normal distribution. Em: Advances in mathematical and statistical modeling,

Springer, pp. 353–360.

Klein, J. P., Moeschberger, M. L. (1997). Survival Analysis: Techniques for Censored and Truncated Data. Springer-

Verlag, New York.

Kozubowski, T. J., Inusah, S. (2006). A skew Laplace distribution on integers. Annals of the Institute of Statistical

Mathematics, 58 (3), 555–571.

Krishna, H., Pundir, P. S. (2009). Discrete Burr and discrete Pareto distributions. Statistical Methodology, 6 (2),

–188.

Kulasekera, K., Tonkyn, D. W. (1992). A new discrete distribution, with applications to survival, dispersal and

dispersion. Communications in Statistics-Simulation and Computation, 21 (2), 499–518.

Lawless, J. F. (2003). Statistical models and methods for lifetime data, 2o edn. Wiley Series in Probability and Statistics,

Wiley-Interscience [John Wiley & Sons], Hoboken, NJ.

Lee, E. T., Wang, J. W. (2003). Statistical methods for survival data analysis, 3o edn. Wiley Series in Probability and

Statistics, Hoboken, NJ.

Liu, W., Cela, J. (2008). Count data models in SAS. Em: SAS Global Forum, Citeseer, vol 317, pp. 1–12.

Long, J. S. (1990). The origins of sex differences in science. Social forces, 68 (4), 1297–1316.

Long, J. S., Freese, J., et al. (2001). Predicted probabilities for count models. Stata Journal, 1 (1), 51–7.

Meeker, W. Q., Escobar, L. A. (1998). Statistical Methods for Reliability Data. John Wiley & Sons, New York.

Nakagawa, T., Osaki, S. (1975). The discrete Weibull distribution. IEEE Transactions on Reliability, 5, 300–301.

Nekoukhou, V., Alamatsaz, M. H., Bidram, H. (2012). A discrete analog of the generalized exponential distribution.

Communication in Statistics- Theory and Methods, 41, 2000–2013.

Nekoukhou, V., Alamatsaz, M. H., Bidram, H. (2013). Discrete generalized exponential distribution of a second type.

Statistics, 47, 876–887.

Roy, D. (2003). The discrete normal distribution. Communication in Statistics- Theory and Methods, 32, 1871–1883.

Roy, D. (2004). Discrete Rayleigh distribution. Reliability, IEEE Transactions on, 53 (2), 255–260.

Sato, H., Ikota, M., Sugimoto, A., Masuda, H. (1999). A new defect distribution metrology with a consistent discrete

exponential formula and its applications. Semiconductor Manufacturing, IEEE Transactions on, 12 (4), 409–418.

Siromoney, G. (1964). The general Dirichlet’s series distribution. Journal of the Indian Statistical Association, 2.

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Published

2017-11-18

How to Cite

Oliveira, R. P., Mazucheli, J., & Achcar, J. A. (2017). A Comparative Study Between Two Discrete Lindley Distributions. Ciência E Natura, 39(3), 539–552. https://doi.org/10.5902/2179460X25186

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Section

Statistics