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Extended Intervened Geometric Distribution

Received: 3 March 2016    Accepted: 29 March 2016    Published: 25 April 2016
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Abstract

Here we develop an extended version of the modified intervened geometric distribution of Kumar and Sreeja (The Aligarh Journal of Statistics, 2014) and investigate some of its important statistical properties. Parameters of the distribution are estimated by various methods of estimation such as the method of factorial moments, the method of mixed moments and the method of maximum likelihood. The distribution has been fitted to a real life data set for illustrating its practical relevance.

Published in International Journal of Statistical Distributions and Applications (Volume 2, Issue 1)
DOI 10.11648/j.ijsd.20160201.12
Page(s) 8-13
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Factorial Moments, Intervened Geometric Distribution, Method of Factorial Moments, Method of Mixed Moments, Method of Maximum Likelihood, Probability Generating Function, Probability Mass Function

References
[1] Shanmugam, R. (1985) An intervened Poisson distribution and its medical application, Biometrics 41, 1025-1029.
[2] Shanmugam, R. (1992) An inferential procedure for the Poisson intervention parameter, Biometrics 48, 559-565.
[3] Haung, M., Fun, K. Y. (1989) Intervened truncated Poisson distributions, Sankhya Series, 51, 302-310.
[4] Scollnik, D. P. M. (2006) On intervened generali-zed Poisson distributions, Communication in Statistics - Theory and Methods, 35, 953-963.
[5] Dhanavanthan, P. (1998) Compound intervened Poisson distributions, Biometrical Journal, 40, 641-646.
[6] Dhanavanthan, P (2000) Estimation of parameters of compound intervened Poisson distributions, Biometrical Journal, 42, 315-320.
[7] Kumar, C. S., Shibu, D. S. (2011) Modified intervened Poisson distributions, Statistica, 71, 489-499.
[8] Kumar, C. S., Shibu, D. S. (2011) Estimation of the parameters of a finite mixture of intervened Poisson distribution. Statistical Methods in Interdisciplinary Studies, Dept. of Statistics, Maharajas College, Cochin, 93-103.
[9] Kumar, C. S., Shibu, D. S. (2012a). Some finite mixtures of intervened Poisson distribution. The Aligarh Journal of Statistics, 32, 97-114.
[10] Kumar, C. S., Shibu, D. S. (2012b). Generalized intervened Poisson distribution. Journal of Applied Statistical Sciences, 19, 131-141.
[11] Kumar, C. S., Shibu, D. S. (2012c). An alternative to truncated intervened Poisson distribution. Journal of Statistics and Applications, 5, 131-141.
[12] Kumar, C. S., Shibu, D. S. (2013) On some aspects of intervened generalized Hermite distribution, Metron, 71, 9-19.
[13] Kumar, C. S., Shibu, D. S. (2013) Finite mixtures of extended intervened Poisson distribution, In Collection of Recent Statistical Methods and Applications, Department of Statistics, University of Kerala Publishers, Trivandrum, 69-82.
[14] Kumar, C. S., Shibu, D. S. (2013) An extended version of intervened Poisson distribution, Research Journal of Fatima Mata National College Kollam, 4, 29-40.
[15] Kumar, C. S., Shibu, D. S. (2014) On finite mixtures of modified intervened Poisson distribution and its applications, Journal of Statistical Theory and Applications, 13(4), 344-355.
[16] Bartolucci, A. A., Shanmugam, R., Singh, K. P. (2001) Development of the generalized geometric model with application to cardiovascular studies, System Analysis, Modelling simulation, 41, 339-349.
[17] Kumar, C. S., Sreejakumari. S. (2014) Modified intervened geometric distribution, The Aligarh Journal of Statistics, 34, 1-12.
[18] Jani, P. N., Shah, S. M. (1979). On fitting of the generalized logarithmic series distribution. Journal of the Indian Society for Agricultural Statistics. 30, 1-10.
Cite This Article
  • APA Style

    C. Satheesh Kumar, S. Sreejakumari. (2016). Extended Intervened Geometric Distribution. International Journal of Statistical Distributions and Applications, 2(1), 8-13. https://doi.org/10.11648/j.ijsd.20160201.12

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    ACS Style

    C. Satheesh Kumar; S. Sreejakumari. Extended Intervened Geometric Distribution. Int. J. Stat. Distrib. Appl. 2016, 2(1), 8-13. doi: 10.11648/j.ijsd.20160201.12

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    AMA Style

    C. Satheesh Kumar, S. Sreejakumari. Extended Intervened Geometric Distribution. Int J Stat Distrib Appl. 2016;2(1):8-13. doi: 10.11648/j.ijsd.20160201.12

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  • @article{10.11648/j.ijsd.20160201.12,
      author = {C. Satheesh Kumar and S. Sreejakumari},
      title = {Extended Intervened Geometric Distribution},
      journal = {International Journal of Statistical Distributions and Applications},
      volume = {2},
      number = {1},
      pages = {8-13},
      doi = {10.11648/j.ijsd.20160201.12},
      url = {https://doi.org/10.11648/j.ijsd.20160201.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20160201.12},
      abstract = {Here we develop an extended version of the modified intervened geometric distribution of Kumar and Sreeja (The Aligarh Journal of Statistics, 2014) and investigate some of its important statistical properties. Parameters of the distribution are estimated by various methods of estimation such as the method of factorial moments, the method of mixed moments and the method of maximum likelihood. The distribution has been fitted to a real life data set for illustrating its practical relevance.},
     year = {2016}
    }
    

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    AU  - S. Sreejakumari
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    T2  - International Journal of Statistical Distributions and Applications
    JF  - International Journal of Statistical Distributions and Applications
    JO  - International Journal of Statistical Distributions and Applications
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    AB  - Here we develop an extended version of the modified intervened geometric distribution of Kumar and Sreeja (The Aligarh Journal of Statistics, 2014) and investigate some of its important statistical properties. Parameters of the distribution are estimated by various methods of estimation such as the method of factorial moments, the method of mixed moments and the method of maximum likelihood. The distribution has been fitted to a real life data set for illustrating its practical relevance.
    VL  - 2
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Author Information
  • Department of Statistics, University of Kerala, Trivandrum, India

  • Department of Statistics, University of Kerala, Trivandrum, India

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