American Journal of Theoretical and Applied Statistics

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Posterior Predictive Checks for the Generalized Pareto Distribution Based on a Dirichlet Process Prior

Received: 14 October 2019    Accepted: 04 December 2019    Published: 25 December 2019
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Abstract

Extreme value modelling is widely applied in situations where accurate assessment of the behavior of a process at high levels is needed. The inherent scarcity of extreme value data, the natural objective of predicting future extreme values of a process associated with modelling of extremes and the regularity assumptions required by the likelihood and probability weighted moments methods of parameter estimation within the frequentist framework, make it imperative for a practitioner to consider Bayesian methodology when modelling extremes. Within the Bayesian paradigm, the widely used tool for assessing the fitness of a model is by using posterior predictive checks (PPCs). The method involves comparing the posterior predictive distribution of future observations to the historical data. Posterior predictive inference involves the prediction of unobserved variables in light of observed data.. This paper considers posterior predictive checks for assessing model fitness for the generalized Pareto model based on a Dirichlet process prior. The posterior predictive distribution for the Dirichlet process based model is derived. Threshold selection is done by minimizing the negative differential entropy of the Dirichlet distribution. Predictions are drawn from the Bayesian posterior distribution by Markov chain Monte Carlo simulation (Metropolis-Hastings Algorithm). Two graphical measures of discrepancy between the predicted observations and the observed values commonly applied in practical extreme value modelling are considered, the cumulative distribution function and quantile plots. To support these, the Nash-Sutcliffe coefficient of model efficiency, a numerical measure that evaluates the error in the predicted observations relative to the natural variation in the observed values is used. Finite sample performance of the proposed procedure is illustrated through simulated data. The results of the study suggest that posterior predictive checks are reasonable diagnostic tools for assessing the fit of the generalized Pareto distribution. In addition, the posterior predictive quantile plot seems to be more informative than the probability plot. Most interestingly, selecting the threshold by minimizing the negative differential entropy of a Dirichlet process has the added advantage of allowing the analyst to estimate the concentration parameter from the model, rather than specifying it as a measure of his/her belief in the proposed model as a prior guess for the unknown distribution that generated the observations. Lastly, the results of application to real life data show that the distribution of the annual maximal inflows into the Okavango River at Mohembo, Botswana, can be adequately described by the generalized Pareto distribution.

DOI 10.11648/j.ajtas.20190806.20
Published in American Journal of Theoretical and Applied Statistics (Volume 8, Issue 6, November 2019)
Page(s) 287-295
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

Dirichlet Process Prior, Generalized Pareto Distribution, Markov Chain Monte Carlo, Peaks Over Threshold, Posterior Predictive Checks

References
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Author Information
  • Department of Statistics, University of Botswana, Gaborone, Botswana

  • Department of Statistics, University of Botswana, Gaborone, Botswana

  • Department of Statistics, University of Botswana, Gaborone, Botswana

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    Wilson Moseki Thupeng, Boikanyo Mokgweetsi, Thuto Mothupi. (2019). Posterior Predictive Checks for the Generalized Pareto Distribution Based on a Dirichlet Process Prior. American Journal of Theoretical and Applied Statistics, 8(6), 287-295. https://doi.org/10.11648/j.ajtas.20190806.20

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    Wilson Moseki Thupeng; Boikanyo Mokgweetsi; Thuto Mothupi. Posterior Predictive Checks for the Generalized Pareto Distribution Based on a Dirichlet Process Prior. Am. J. Theor. Appl. Stat. 2019, 8(6), 287-295. doi: 10.11648/j.ajtas.20190806.20

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    Wilson Moseki Thupeng, Boikanyo Mokgweetsi, Thuto Mothupi. Posterior Predictive Checks for the Generalized Pareto Distribution Based on a Dirichlet Process Prior. Am J Theor Appl Stat. 2019;8(6):287-295. doi: 10.11648/j.ajtas.20190806.20

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  • @article{10.11648/j.ajtas.20190806.20,
      author = {Wilson Moseki Thupeng and Boikanyo Mokgweetsi and Thuto Mothupi},
      title = {Posterior Predictive Checks for the Generalized Pareto Distribution Based on a Dirichlet Process Prior},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {8},
      number = {6},
      pages = {287-295},
      doi = {10.11648/j.ajtas.20190806.20},
      url = {https://doi.org/10.11648/j.ajtas.20190806.20},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20190806.20},
      abstract = {Extreme value modelling is widely applied in situations where accurate assessment of the behavior of a process at high levels is needed. The inherent scarcity of extreme value data, the natural objective of predicting future extreme values of a process associated with modelling of extremes and the regularity assumptions required by the likelihood and probability weighted moments methods of parameter estimation within the frequentist framework, make it imperative for a practitioner to consider Bayesian methodology when modelling extremes. Within the Bayesian paradigm, the widely used tool for assessing the fitness of a model is by using posterior predictive checks (PPCs). The method involves comparing the posterior predictive distribution of future observations to the historical data. Posterior predictive inference involves the prediction of unobserved variables in light of observed data.. This paper considers posterior predictive checks for assessing model fitness for the generalized Pareto model based on a Dirichlet process prior. The posterior predictive distribution for the Dirichlet process based model is derived. Threshold selection is done by minimizing the negative differential entropy of the Dirichlet distribution. Predictions are drawn from the Bayesian posterior distribution by Markov chain Monte Carlo simulation (Metropolis-Hastings Algorithm). Two graphical measures of discrepancy between the predicted observations and the observed values commonly applied in practical extreme value modelling are considered, the cumulative distribution function and quantile plots. To support these, the Nash-Sutcliffe coefficient of model efficiency, a numerical measure that evaluates the error in the predicted observations relative to the natural variation in the observed values is used. Finite sample performance of the proposed procedure is illustrated through simulated data. The results of the study suggest that posterior predictive checks are reasonable diagnostic tools for assessing the fit of the generalized Pareto distribution. In addition, the posterior predictive quantile plot seems to be more informative than the probability plot. Most interestingly, selecting the threshold by minimizing the negative differential entropy of a Dirichlet process has the added advantage of allowing the analyst to estimate the concentration parameter from the model, rather than specifying it as a measure of his/her belief in the proposed model as a prior guess for the unknown distribution that generated the observations. Lastly, the results of application to real life data show that the distribution of the annual maximal inflows into the Okavango River at Mohembo, Botswana, can be adequately described by the generalized Pareto distribution.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Posterior Predictive Checks for the Generalized Pareto Distribution Based on a Dirichlet Process Prior
    AU  - Wilson Moseki Thupeng
    AU  - Boikanyo Mokgweetsi
    AU  - Thuto Mothupi
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    N1  - https://doi.org/10.11648/j.ajtas.20190806.20
    DO  - 10.11648/j.ajtas.20190806.20
    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  - 287
    EP  - 295
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20190806.20
    AB  - Extreme value modelling is widely applied in situations where accurate assessment of the behavior of a process at high levels is needed. The inherent scarcity of extreme value data, the natural objective of predicting future extreme values of a process associated with modelling of extremes and the regularity assumptions required by the likelihood and probability weighted moments methods of parameter estimation within the frequentist framework, make it imperative for a practitioner to consider Bayesian methodology when modelling extremes. Within the Bayesian paradigm, the widely used tool for assessing the fitness of a model is by using posterior predictive checks (PPCs). The method involves comparing the posterior predictive distribution of future observations to the historical data. Posterior predictive inference involves the prediction of unobserved variables in light of observed data.. This paper considers posterior predictive checks for assessing model fitness for the generalized Pareto model based on a Dirichlet process prior. The posterior predictive distribution for the Dirichlet process based model is derived. Threshold selection is done by minimizing the negative differential entropy of the Dirichlet distribution. Predictions are drawn from the Bayesian posterior distribution by Markov chain Monte Carlo simulation (Metropolis-Hastings Algorithm). Two graphical measures of discrepancy between the predicted observations and the observed values commonly applied in practical extreme value modelling are considered, the cumulative distribution function and quantile plots. To support these, the Nash-Sutcliffe coefficient of model efficiency, a numerical measure that evaluates the error in the predicted observations relative to the natural variation in the observed values is used. Finite sample performance of the proposed procedure is illustrated through simulated data. The results of the study suggest that posterior predictive checks are reasonable diagnostic tools for assessing the fit of the generalized Pareto distribution. In addition, the posterior predictive quantile plot seems to be more informative than the probability plot. Most interestingly, selecting the threshold by minimizing the negative differential entropy of a Dirichlet process has the added advantage of allowing the analyst to estimate the concentration parameter from the model, rather than specifying it as a measure of his/her belief in the proposed model as a prior guess for the unknown distribution that generated the observations. Lastly, the results of application to real life data show that the distribution of the annual maximal inflows into the Okavango River at Mohembo, Botswana, can be adequately described by the generalized Pareto distribution.
    VL  - 8
    IS  - 6
    ER  - 

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