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

Wesonga Ronald; Nabugoomu Fabian

doi:doi:10.11648/j.ajtas.20140301.11

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

Wesonga Ronald, Nabugoomu Fabian

Published in American Journal of Theoretical and Applied Statistics (Volume 3, Issue 1)

Received: 9 November 2013 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.

Published in	American Journal of Theoretical and Applied Statistics (Volume 3, Issue 1)
DOI	10.11648/j.ajtas.20140301.11
Page(s)	1-5
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), 2013. Published by Science Publishing Group

Keywords

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

References

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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://article.sciencepublishinggroup.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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TY  - JOUR
T1  - Bayesian Model Averaging: An Application to the Determinants of Airport Departure Delay in Uganda
AU  - Wesonga Ronald
AU  - Nabugoomu Fabian
Y1  - 2013/12/10
PY  - 2013
N1  - https://doi.org/10.11648/j.ajtas.20140301.11
DO  - 10.11648/j.ajtas.20140301.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 1
EP  - 5
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20140301.11
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.
VL  - 3
IS  - 1
ER  -

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Author Information

Wesonga Ronald

School of Statistics and Planning, Makerere University, Kampala, Uganda
Nabugoomu Fabian

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
T1  - Bayesian Model Averaging: An Application to the Determinants of Airport Departure Delay in Uganda
AU  - Wesonga Ronald
AU  - Nabugoomu Fabian
Y1  - 2013/12/10
PY  - 2013
N1  - https://doi.org/10.11648/j.ajtas.20140301.11
DO  - 10.11648/j.ajtas.20140301.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 1
EP  - 5
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20140301.11
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.
VL  - 3
IS  - 1
ER  -

Copy | Download