On Testing the Adequacy of the Lindley Model and Power Study

  • Hadi Alizadeh Noughabi University of Birjand
  • Mohammad Shafaei Noughabi Department of Mathematics and Statistics, University of Gonabad, Gonabad, Iran
Keywords: Empirical distribution function, Model validity, Goodness of fit tests, Lindley distribution, Monte Carlo simulation, Power study

Abstract

The Lindley distribution may serve as a useful reliability model. Applications of this distribution are presented in statistical literature. In this article, goodness of fit tests for the Lindley distribution based on the empirical distribution function (EDF) are considered. In order to compute the test statistics, we use the maximum likelihood estimate (MLE) suggested by Ghitany et al. (2008), which is simple explicit estimator. Critical points of the proposed test statistics are obtained by Monte Carlo simulation. Power comparisons of the considered tests are carried out via simulations. Finally, two illustrative examples are presented and analyzed.

References

Ali, S., Aslam, M., Kazmi, S. (2013), A study of the effect of the loss function on Bayes Estimate, posterior risk and hazard function for Lindley distribution, Applied Mathematical Modelling, 37, 6068–6078.

Alizadeh Noughabi, H. (2015), Testing exponentiality based on the likelihood ratio and power comparison, Annals of Data Science, 2, 195–204.

Alizadeh Noughabi, H. (2016), Two powerful tests for normality, Annals of Data Science, 3, 225–234.

Alizadeh Noughabi, H. and Balakrishnan, N. (2015), Goodness of fit using a new estimate of Kullback-Leibler information based on type II censored data, IEEE Transactions on Reliability, 64, 627-635.

Alizadeh Noughabi, H. and Chahkandi, M. (2015), Informational energy and its application in testing normality, Annals of Data Science, 2, 391–401.

Al-Mutairi, D.K., Ghitany, M.E., Kundu, D. (2013), Inferences on the stress-strength reliability from Lindley distributions,

Communications in Statistics-Theory and Methods, 42, 1443–1463.

Altun, E. (2019), Two-sided Lindley distribution with inference and applications, Journal of the Indian Society for Probability and Statistics, 20, 255–279.

Anderson, T.W. and Darling, D.A. (1954), A test of goodness of fit, Journal of American Statistical Association, 49, 765-769.

Balakrishnan, N., Habibi Rad, A. and Arghami, N.R. (2007), Testing exponentiality based on Kullback–Leibler information with progressively type-II censored data, IEEE Transactions on Reliability, 56, 301–307.

Balakrishnan, N., Ng, H.K.T. and Kannan, N. (2004), Goodness-of-fit tests based on spacings for progressively type-II censored data from a general location-scale distribution, IEEE Transactions on Reliability, 53, 349–356.

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

Chesneau, C., Tomy, L., Gillariose, J. (2021), A new modified Lindley distribution with properties and applications, Journal of Statistics and Management Systems, 24(7), 1383-1403.

Chesneau, C., Tomy, L., Jose, M. (2021a), Wrapped modified Lindley distribution, Journal of Statistics and Management Systems, 24 (5), 1025-1040.

D’Agostino, R.B. and Stephens, M.A. (Eds.) (1986), Goodness-of-fit Techniques, New York: Marcel Dekker.

Ghitany, M.E., Atieh, B. and Nadarajah, S. (2008), Lindley distribution and its Application, Mathematics Computing and Simulation, 78, 493-506.

Gupta, PK, Singh, B. (2013), Parameter estimation of Lindley distribution with hybrid censored data, International Journal of System Assurance Engineering and Management, 4, 378–385.

Habibi Rad, A., Yousefzadeh, F. and Balakrishnan, N. (2011), Goodness-of-fit test based on Kullback–Leibler information for progressively Type-II censored data, IEEE Transactions on Reliability, 60, 570–579.

He, D.J. and Xu, X.Z. (2013), A goodness-of-fit testing approach for normality based on the posterior predictive distribution, Test, 22, 1-18.

Huber-Carol, C., Balakrishnan, N., Nikulin, M. S. and Mesbah, M. (2002), Goodness-of-fit tests and model validity, Boston, Basel, Berlin: Birkh¨auser.

Ibrahim M., Yadav, A.S., Yousof, H.M., Goual, H., Hamedani, G. (2019), A new extension of Lindley distribution: modified validation test, characterizations and different methods of estimation, Communications for Statistical Applications and Methods, 26:473-495.

Jahanshahi, S. M. A., Habibi Rad, A., Fakoor, V. (2016), A goodness-of-fit test for Rayleigh distribution based on Hellinger distance, Annals of Data Science, 3, 401–411.

Kolmogorov, A.N. (1933), Sulla Determinazione Empirica di une legge di Distribuzione. Giornale dell’Intituto Italiano degli Attuari, 4, 83-91.

Krishna, H. and Kumar, K. (2011), Reliability estimation in Lindley distribution with progressively type II right censored sample, Mathematics and Computers in Simulation, 82, 281–294.

Kuiper, N.H. (1960), Tests concerning random points on a circle, Proceedings of the Koninklijke Nederlandse Akademie van Wetenschappen, Series A, 63, 38-47.

Kumar, C.S. and Jose, R. (2019), On double Lindley distribution and some of its properties, American Journal of Mathematical and Management Sciences, 38, 23-43.

Kumar, K., Krishna, H. and Garg, R. (2015), Estimation of P(Y ¡ X) in Lindley distribution using progressively first failure censoring, International Journal of System Assurance Engineering and Management, 6, 330–341.

Lawless, J.F. (1982), Statistical models and methods for lifetime data. In: John Wiley & Sons (Ed.), New York, USA.

Lin, C-T., Huang, Y-L. and Balakrishnan, N. (2008), A new method for goodness-of-fit testing based on Type-II right censored samples, IEEE Transactions on Reliability, 57, 633–642.

Lindley, D.V. (1958), Fiducial distribution and Bayes’ theorem, Journal of the Royal Statistical Society, 20, 102–107.

Mazucheli, J. and Achcar, J.A. (2011), The Lindley distribution applied to competing risks lifetime data, Computer Methods and Programs in Biomedicine, 2011, 104, 188- 192.

Pakyari, R. and Balakrishnan, N. (2012), A general purpose approximate goodness-of-fit test for progressively Type-II censored data, IEEE Transactions on Reliability, 61, 238–244.

Pakyari, R. and Balakrishnan, N. (2013), Goodness-of-fit tests for progressively Type-II censored data from location-scale distributions, Journal of Statistical Computation and Simulation, 83, 167-178.

Shanker, R., Hagos, F., Sujatha, S. (2015), On modeling of Lifetimes data using exponential and Lindley distributions, Biometrics & Biostatistics International Journal, 2, 1–9.

Singh, B. and Gupta, P.K. (2012), Load-sharing system model and its application to the real data set, Mathematics and Computers in Simulation, 82, 1615–1629.

Tomy, L. (2018), A retrospective study on Lindley distribution, Biometrics & Biostatistics International Journal, 7(3), 163-169.

Tomy, L., Veena, G., Chesneau, C. (2021b), The sine modified Lindley distribution, Mathematical and Computational Applications, 26 (81), 1-15.

Valiollahi, R., Asgharzadeh, A. and Ng, H.K.T. (2017), Prediction for Lindley distribution based on type-II right censored samples, Journal of the Iranian Statistical Society, 16, 1-19.

von Mises, R. (1931), Wahrscheinlichkeitsrechnung und ihre Anwendung in der Statistik und theoretischen Physik, Leipzig and Vienna: Deuticke.

Wang, M. (2013), A new three-parameter lifetime distribution and associated inference. arXiv:1308.4128 [stat.ME].

Watson, G.S. (1961), Goodness of fit tests on a circle, Biometrika, 48, 109-114.

Zhang, J. (2002), Powerful goodness-of-fit tests based on the likelihood ratio, Journal of Royal Statistical Society, Series B, 64, 281-294.

Published
2023-04-20
How to Cite
Alizadeh Noughabi, H., & Noughabi, M. S. (2023). On Testing the Adequacy of the Lindley Model and Power Study. Statistics, Optimization & Information Computing, 11(3), 719-729. https://doi.org/10.19139/soic-2310-5070-1443
Section
Research Articles