Research Article
BibTex RIS Cite
Year 2016, Volume: 4 Issue: 1, 94 - 112, 15.04.2016
https://doi.org/10.36753/mathenot.421415

Abstract

References

  • [1] Ahsanullah, M., On lower generalized order statistics and a characterization of power function distribution. Stat. Methods, 7 (2005), 16-28.
  • [2] Arnold, B. C., Balakrishnan, N. and Nagaraja, H. N., A First Course in Order Statistics. John Wiley, New York, (1992).
  • [3] Balakrishnan, N. and Cohan, A.C., Order Statistics and Inference: Estimation Methods. Academic Press, San Diego, (1991).
  • [4] Brown, M., Low Density Traffic Streams. Advances in Applied Probability, 4 (1972), 177-192.
  • [5] Bieniek, M. and Szynal, D., Characterizations of distributions via linearity of regression of generalized order statistics. Metrika, 58 (2003), 259-271.
  • [6] Burkschat, M., Cramer, E. and Kamps, U., Dual generalized order statistics. Metron, LXI (2003), 13-26.
  • [7] Calabria, R. and Pulcini, G., Point estimation under asymmetric loss functions for left-truncated exponential samples. Comm. Statist. Theory Methods, 25 (1996), 585-600.
  • [8] Chandler, K. N., The distribution and frequency of record values, J. Roy. Stat. Soc. B. 14 (1952), 220-228.
  • [9] Cramer, E., Kamps, U. and Keseling, C., Characterization via linear regression of ordered random variables: a unifying approach. Communications in Statistics-Theory and Methods, 33 (2004), 2885-2911.
  • [10] Dara, S.T. and Ahmad, M., Recent Advances in Moment Distributions and their Hazard Rate. Ph.D. Thesis. National College of Business Administration and Economics, Lahore, Pakistan, (2012).
  • [11] Gupta, R.D. and Kundu, D.,Generalized exponential distributions; An alternative to gamma or Weibull distribution. Biometrical Journal, 43(2001), 117-130.
  • [12] Gupta, R. D. and Kundu, D., On the comparison of Fisher information of the Weibull and generalized exponential distributions, Journal of Statistical Planning and Inference, 136 (2006), 3130-3144.
  • [13] Gy, P., Sampling of Particulate Material: Theory and Practice. Elsevier: Amsterdam, 431 (1982).
  • [14] Hasnain, S. A., Exponentiated moment Exponential Distribution. Ph.D. Thesis. National College of Business Administration and Economics, Lahore, Pakistan, (2013).
  • [15] Jeffreys, H., An invariant form for the prior probability in estimation problems, Proceedings of Royal Society of London. Series A, Mathematical and Physical Sciences 186(1946), 435-461.
  • [16] Kamps, U., A concept of generalized order statistics. B.G. Teubner Stuttgart, (1995).
  • [17] Keseling, C., Conditional distributions of generalized order statistics and some characterizations. Metrika, 49 (1999), 27-40.
  • [18] Khan, R.U. and Kumar, D., On moments of generalized order statistics from exponentiated Pareto distribution and its characterization. Appl. Math. Sci. (Ruse), 4 (2010), 2711-2722.
  • [19] Krumbein, W.C. and Pettijohn, F. J. Manual of sedimentary petrography. Appleton Century-Crofts, Inc., New York, 230-233, (1938).
  • [20] Kumar, D., Relations for moments of lower generalized order statistics from exponentiated inverted Weibull distribution. Tamsui Oxford journal of Mathematical Science. 30 (2014), 1-21.
  • [21] Kumar, D., Exponentiated Gamma Distribution Based On Ordered Random Variables. Applied Mathematics and E-Notes, 15 (2015), 105-120.
  • [22] Kumar, D., Lower Generalized Order Statistics Based On Inverse Burr Distribution, American Journal of Mathematical and Management Sciences, 35 (2015), 15-35.
  • [23] Lawless, J. F., Statistical models and methods for lifetime data, 2nd Edition, Wiley, New York, (1982). [24] Lin, G. D., On a moment problem. Tohoku Math. Journal, 38 (1986), 595-598.
  • [25] Martz, H. F. and Waller, R. A., Bayesian Reliability Analysis, Wiley, New York, (1982).
  • [26] Mbah, A.K. and Ahsanullah, M., Some characterization of the power function distribution based on lower generalized order statistics. Pakistan J. Statist., 23(2007), 139-146.
  • [27] Metropolis, N., Rosenbluth, A. W., Rosebluth, M. N., Teller, A. H. and Teller, E., Equations of state calculations by fast computing machines, Journal of Chemical Physics, 21 (1953), 1087-1092.
  • [28] Pawlas, P. and Szynal, D., Recurrence relations for single and product moments of lower generalized order statistics from the inverse Weibull distribution. Demonstratio Math., XXXIV (2001), 353-358.
  • [29] Preston, F.W., The canonical distribution of commonness and rarity, Ecology 43 (1962), 186-215.
  • [30] Taillie, C., Patil, G.P. and Hennemuth, R.,Modelling and analysis of recruitment distributions. Ecological and Environmental Statistics, 2 (1995), 315-329.
  • [31] Temkin, N., Interactive information and distributional length biased survival models. Unpublished Ph.D. dissertation, University of New York at Buffalo, (1976).
  • [32] Warren, W., Statistical distributions in forestry and forest products research. In: Patil, G.P, Kotz, S. and Ord, J.K. (eds) Statistical distributions in scientific work, Vol. 2, D. Reidel, Dordrecht, The Netherlands, 360-384, (1975).
  • [33] Zelen, M., Problems in cell kinetics and the early detection of disease, in Reliability and Biometry, F. Proschan & R.J. Serfling, eds, SIAM, Philadelphia, 701-706, (1974).
  • [34] Zelen M., Theory of early detection of breast cancer in the general population, Breast Cancer: Trends in Research and Treatment (ed. J.C. Heuson, W.H. Mattheiem, M. Rozenweig), Raven Press, New York, 287-301, (1976).
  • [35] Zheng, G., On the Fisher information matrix in type II censored data from the exponentiated exponential family, Biometrical Journal, 44(2002), 353-357.

Moments and Estimation of the Exponentiated Moment Exponential Distribution

Year 2016, Volume: 4 Issue: 1, 94 - 112, 15.04.2016
https://doi.org/10.36753/mathenot.421415

Abstract

A new extension of moment exponential distribution, called exponentiated moment exponential distribution
(EMED), was recently introduced by Hasnain [14]. Based on lower generalized order statistics, we
first derive the explicit expressions as well as recurrence relations for single and product moments of lower
generalized order statistics and we use these results to compute the means, variances and coefficients of
skewness and kurtosis of EMED. Further, using a recurrence relation for single moment, we obtain characterization
of EMED. Next we obtain the maximum likelihood estimators of the unknown parameters
and the approximate confidence intervals of the EMED. Finally, we consider Bayes estimation under the
symmetric and asymmetric loss functions using gamma priors for both shape and scale parameters. We
have are also derived the Bayes interval of this distribution. Monte Carlo simulations are performed to
compare the performances of the proposed methods. 

References

  • [1] Ahsanullah, M., On lower generalized order statistics and a characterization of power function distribution. Stat. Methods, 7 (2005), 16-28.
  • [2] Arnold, B. C., Balakrishnan, N. and Nagaraja, H. N., A First Course in Order Statistics. John Wiley, New York, (1992).
  • [3] Balakrishnan, N. and Cohan, A.C., Order Statistics and Inference: Estimation Methods. Academic Press, San Diego, (1991).
  • [4] Brown, M., Low Density Traffic Streams. Advances in Applied Probability, 4 (1972), 177-192.
  • [5] Bieniek, M. and Szynal, D., Characterizations of distributions via linearity of regression of generalized order statistics. Metrika, 58 (2003), 259-271.
  • [6] Burkschat, M., Cramer, E. and Kamps, U., Dual generalized order statistics. Metron, LXI (2003), 13-26.
  • [7] Calabria, R. and Pulcini, G., Point estimation under asymmetric loss functions for left-truncated exponential samples. Comm. Statist. Theory Methods, 25 (1996), 585-600.
  • [8] Chandler, K. N., The distribution and frequency of record values, J. Roy. Stat. Soc. B. 14 (1952), 220-228.
  • [9] Cramer, E., Kamps, U. and Keseling, C., Characterization via linear regression of ordered random variables: a unifying approach. Communications in Statistics-Theory and Methods, 33 (2004), 2885-2911.
  • [10] Dara, S.T. and Ahmad, M., Recent Advances in Moment Distributions and their Hazard Rate. Ph.D. Thesis. National College of Business Administration and Economics, Lahore, Pakistan, (2012).
  • [11] Gupta, R.D. and Kundu, D.,Generalized exponential distributions; An alternative to gamma or Weibull distribution. Biometrical Journal, 43(2001), 117-130.
  • [12] Gupta, R. D. and Kundu, D., On the comparison of Fisher information of the Weibull and generalized exponential distributions, Journal of Statistical Planning and Inference, 136 (2006), 3130-3144.
  • [13] Gy, P., Sampling of Particulate Material: Theory and Practice. Elsevier: Amsterdam, 431 (1982).
  • [14] Hasnain, S. A., Exponentiated moment Exponential Distribution. Ph.D. Thesis. National College of Business Administration and Economics, Lahore, Pakistan, (2013).
  • [15] Jeffreys, H., An invariant form for the prior probability in estimation problems, Proceedings of Royal Society of London. Series A, Mathematical and Physical Sciences 186(1946), 435-461.
  • [16] Kamps, U., A concept of generalized order statistics. B.G. Teubner Stuttgart, (1995).
  • [17] Keseling, C., Conditional distributions of generalized order statistics and some characterizations. Metrika, 49 (1999), 27-40.
  • [18] Khan, R.U. and Kumar, D., On moments of generalized order statistics from exponentiated Pareto distribution and its characterization. Appl. Math. Sci. (Ruse), 4 (2010), 2711-2722.
  • [19] Krumbein, W.C. and Pettijohn, F. J. Manual of sedimentary petrography. Appleton Century-Crofts, Inc., New York, 230-233, (1938).
  • [20] Kumar, D., Relations for moments of lower generalized order statistics from exponentiated inverted Weibull distribution. Tamsui Oxford journal of Mathematical Science. 30 (2014), 1-21.
  • [21] Kumar, D., Exponentiated Gamma Distribution Based On Ordered Random Variables. Applied Mathematics and E-Notes, 15 (2015), 105-120.
  • [22] Kumar, D., Lower Generalized Order Statistics Based On Inverse Burr Distribution, American Journal of Mathematical and Management Sciences, 35 (2015), 15-35.
  • [23] Lawless, J. F., Statistical models and methods for lifetime data, 2nd Edition, Wiley, New York, (1982). [24] Lin, G. D., On a moment problem. Tohoku Math. Journal, 38 (1986), 595-598.
  • [25] Martz, H. F. and Waller, R. A., Bayesian Reliability Analysis, Wiley, New York, (1982).
  • [26] Mbah, A.K. and Ahsanullah, M., Some characterization of the power function distribution based on lower generalized order statistics. Pakistan J. Statist., 23(2007), 139-146.
  • [27] Metropolis, N., Rosenbluth, A. W., Rosebluth, M. N., Teller, A. H. and Teller, E., Equations of state calculations by fast computing machines, Journal of Chemical Physics, 21 (1953), 1087-1092.
  • [28] Pawlas, P. and Szynal, D., Recurrence relations for single and product moments of lower generalized order statistics from the inverse Weibull distribution. Demonstratio Math., XXXIV (2001), 353-358.
  • [29] Preston, F.W., The canonical distribution of commonness and rarity, Ecology 43 (1962), 186-215.
  • [30] Taillie, C., Patil, G.P. and Hennemuth, R.,Modelling and analysis of recruitment distributions. Ecological and Environmental Statistics, 2 (1995), 315-329.
  • [31] Temkin, N., Interactive information and distributional length biased survival models. Unpublished Ph.D. dissertation, University of New York at Buffalo, (1976).
  • [32] Warren, W., Statistical distributions in forestry and forest products research. In: Patil, G.P, Kotz, S. and Ord, J.K. (eds) Statistical distributions in scientific work, Vol. 2, D. Reidel, Dordrecht, The Netherlands, 360-384, (1975).
  • [33] Zelen, M., Problems in cell kinetics and the early detection of disease, in Reliability and Biometry, F. Proschan & R.J. Serfling, eds, SIAM, Philadelphia, 701-706, (1974).
  • [34] Zelen M., Theory of early detection of breast cancer in the general population, Breast Cancer: Trends in Research and Treatment (ed. J.C. Heuson, W.H. Mattheiem, M. Rozenweig), Raven Press, New York, 287-301, (1976).
  • [35] Zheng, G., On the Fisher information matrix in type II censored data from the exponentiated exponential family, Biometrical Journal, 44(2002), 353-357.
There are 34 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Devendra Kumar

Publication Date April 15, 2016
Submission Date March 4, 2015
Published in Issue Year 2016 Volume: 4 Issue: 1

Cite

APA Kumar, D. (2016). Moments and Estimation of the Exponentiated Moment Exponential Distribution. Mathematical Sciences and Applications E-Notes, 4(1), 94-112. https://doi.org/10.36753/mathenot.421415
AMA Kumar D. Moments and Estimation of the Exponentiated Moment Exponential Distribution. Math. Sci. Appl. E-Notes. April 2016;4(1):94-112. doi:10.36753/mathenot.421415
Chicago Kumar, Devendra. “Moments and Estimation of the Exponentiated Moment Exponential Distribution”. Mathematical Sciences and Applications E-Notes 4, no. 1 (April 2016): 94-112. https://doi.org/10.36753/mathenot.421415.
EndNote Kumar D (April 1, 2016) Moments and Estimation of the Exponentiated Moment Exponential Distribution. Mathematical Sciences and Applications E-Notes 4 1 94–112.
IEEE D. Kumar, “Moments and Estimation of the Exponentiated Moment Exponential Distribution”, Math. Sci. Appl. E-Notes, vol. 4, no. 1, pp. 94–112, 2016, doi: 10.36753/mathenot.421415.
ISNAD Kumar, Devendra. “Moments and Estimation of the Exponentiated Moment Exponential Distribution”. Mathematical Sciences and Applications E-Notes 4/1 (April 2016), 94-112. https://doi.org/10.36753/mathenot.421415.
JAMA Kumar D. Moments and Estimation of the Exponentiated Moment Exponential Distribution. Math. Sci. Appl. E-Notes. 2016;4:94–112.
MLA Kumar, Devendra. “Moments and Estimation of the Exponentiated Moment Exponential Distribution”. Mathematical Sciences and Applications E-Notes, vol. 4, no. 1, 2016, pp. 94-112, doi:10.36753/mathenot.421415.
Vancouver Kumar D. Moments and Estimation of the Exponentiated Moment Exponential Distribution. Math. Sci. Appl. E-Notes. 2016;4(1):94-112.

20477

The published articles in MSAEN are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.