American Journal of Theoretical and Applied Statistics

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Bayesian Model Averaging: An Application to the Determinants of Airport Departure Delay in Uganda

Received: 09 November 2013    Accepted:     Published: 10 December 2013
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Abstract

Bayesian model averaging was employed to study the dynamics of aircraft departure delay based on airport operational data of aviation and meteorological parameters collected on daily basis for the period 2004 through 2008 in matrix X. Models were evaluated using the R programming language mainly to establish the combinations of variables that could formulate the best model through assessing their importance. Findings showed that out of the sixteen covariates, 62.5% were suitable for model inclusion to determine aircraft departure delay of which 40% exhibited negative coefficients. The following parameters were found to negatively affect departure delay; number of aircrafts that departed on time (-0.562), number of persons on board of the arriving aircrafts (-0.002), daily average visibility (-0.001) and year (-1.605). Comparison between Posterior Model Probabilities (PMP Exact) and that based on Markov Chain Monte Carlo (PMP MCMC) revealed a high correlation (0.998; p<0.01).The study recommended the MCMC as providing a more efficient approach to modelling the determinants of aircraft departure delay at an airport.

DOI 10.11648/j.ajtas.20140301.11
Published in American Journal of Theoretical and Applied Statistics (Volume 3, Issue 1, January 2014)
Page(s) 1-5
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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

Airport Departure Delay, Prior, Posterior Model Probability, Model Selection

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[5] R. Wesonga, F. Nabugoomu, and P. Jehopio, "Parameterized framework for the analysis of probabilities of aircraft delay at an airport," Journal of Air Transport Management, vol. 23, pp. 1-4, 2012.
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[16] N. Xu, G. Donohue, K. B. Laskey, and C.-H. Chen, "Estimation of delay propagation in the national aviation system using Bayesian networks," in 6th USA/Europe Air Traffic Management Research and Development Seminar, 2005.
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Author Information
  • School of Statistics and Planning, Makerere University, Kampala, Uganda

  • Kyambogo University, Kampala, Uganda

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  • APA Style

    Wesonga Ronald, Nabugoomu Fabian. (2013). Bayesian Model Averaging: An Application to the Determinants of Airport Departure Delay in Uganda. American Journal of Theoretical and Applied Statistics, 3(1), 1-5. https://doi.org/10.11648/j.ajtas.20140301.11

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    ACS Style

    Wesonga Ronald; Nabugoomu Fabian. Bayesian Model Averaging: An Application to the Determinants of Airport Departure Delay in Uganda. Am. J. Theor. Appl. Stat. 2013, 3(1), 1-5. doi: 10.11648/j.ajtas.20140301.11

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    AMA Style

    Wesonga Ronald, Nabugoomu Fabian. Bayesian Model Averaging: An Application to the Determinants of Airport Departure Delay in Uganda. Am J Theor Appl Stat. 2013;3(1):1-5. doi: 10.11648/j.ajtas.20140301.11

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  • @article{10.11648/j.ajtas.20140301.11,
      author = {Wesonga Ronald and Nabugoomu Fabian},
      title = {Bayesian Model Averaging: An Application to the Determinants of Airport Departure Delay in Uganda},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {3},
      number = {1},
      pages = {1-5},
      doi = {10.11648/j.ajtas.20140301.11},
      url = {https://doi.org/10.11648/j.ajtas.20140301.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20140301.11},
      abstract = {Bayesian model averaging was employed to study the dynamics of aircraft departure delay based on airport operational data of aviation and meteorological parameters collected on daily basis for the period 2004 through 2008 in matrix X. Models were evaluated using the R programming language mainly to establish the combinations of variables that could formulate the best model through assessing their importance. Findings showed that out of the sixteen covariates, 62.5% were suitable for model inclusion to determine aircraft departure delay of which 40% exhibited negative coefficients. The following parameters were found to negatively affect departure delay; number of aircrafts that departed on time (-0.562), number of persons on board of the arriving aircrafts (-0.002), daily average visibility (-0.001) and year (-1.605). Comparison between Posterior Model Probabilities (PMP Exact) and that based on Markov Chain Monte Carlo (PMP MCMC) revealed a high correlation (0.998; p<0.01).The study recommended the MCMC as providing a more efficient approach to modelling the determinants of aircraft departure delay at an airport.},
     year = {2013}
    }
    

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    DO  - 10.11648/j.ajtas.20140301.11
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
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    AB  - Bayesian model averaging was employed to study the dynamics of aircraft departure delay based on airport operational data of aviation and meteorological parameters collected on daily basis for the period 2004 through 2008 in matrix X. Models were evaluated using the R programming language mainly to establish the combinations of variables that could formulate the best model through assessing their importance. Findings showed that out of the sixteen covariates, 62.5% were suitable for model inclusion to determine aircraft departure delay of which 40% exhibited negative coefficients. The following parameters were found to negatively affect departure delay; number of aircrafts that departed on time (-0.562), number of persons on board of the arriving aircrafts (-0.002), daily average visibility (-0.001) and year (-1.605). Comparison between Posterior Model Probabilities (PMP Exact) and that based on Markov Chain Monte Carlo (PMP MCMC) revealed a high correlation (0.998; p<0.01).The study recommended the MCMC as providing a more efficient approach to modelling the determinants of aircraft departure delay at an airport.
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