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Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation

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  • C. C. Drovandi
  • A. N. Pettitt

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  • C. C. Drovandi & A. N. Pettitt, 2011. "Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation," Biometrics, The International Biometric Society, vol. 67(1), pages 225-233, March.
    • Handle: RePEc:bla:biomet:v:67:y:2011:i:1:p:225-233
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2010.01410.x
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    1. Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
    2. Knut Heggland & Arnoldo Frigessi, 2004. "Estimating functions in indirect inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 447-462, May.
    3. Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Biometrika Trust, vol. 89(3), pages 539-552, August.
    4. Mark A. Beaumont & Jean-Marie Cornuet & Jean-Michel Marin & Christian P. Robert, 2009. "Adaptive approximate Bayesian computation," Biometrika, Biometrika Trust, vol. 96(4), pages 983-990.
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    Cited by:

    1. Muchmore Patrick & Marjoram Paul, 2015. "Exact likelihood-free Markov chain Monte Carlo for elliptically contoured distributions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(4), pages 317-332, August.
    2. Elizabeth G. Ryan & Christopher C. Drovandi & James M. McGree & Anthony N. Pettitt, 2016. "A Review of Modern Computational Algorithms for Bayesian Optimal Design," International Statistical Review, International Statistical Institute, vol. 84(1), pages 128-154, April.
    3. Prangle Dennis & Fearnhead Paul & Cox Murray P. & Biggs Patrick J. & French Nigel P., 2014. "Semi-automatic selection of summary statistics for ABC model choice," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(1), pages 67-82, February.
    4. Sun, Libo & Lee, Chihoon & Hoeting, Jennifer A., 2015. "A penalized simulated maximum likelihood approach in parameter estimation for stochastic differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 54-67.
    5. Silk Daniel & Filippi Sarah & Stumpf Michael P. H., 2013. "Optimizing threshold-schedules for sequential approximate Bayesian computation: applications to molecular systems," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(5), pages 603-618, October.
    6. Anthony Ebert & Kerrie Mengersen & Fabrizio Ruggeri & Paul Wu, 2021. "Curve Registration of Functional Data for Approximate Bayesian Computation," Stats, MDPI, vol. 4(3), pages 1-14, September.
    7. Drovandi, Christopher C. & McGree, James M. & Pettitt, Anthony N., 2013. "Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 320-335.
    8. Henri Pesonen & Umberto Simola & Alvaro Köhn‐Luque & Henri Vuollekoski & Xiaoran Lai & Arnoldo Frigessi & Samuel Kaski & David T. Frazier & Worapree Maneesoonthorn & Gael M. Martin & Jukka Corander, 2023. "ABC of the future," International Statistical Review, International Statistical Institute, vol. 91(2), pages 243-268, August.
    9. Filippi Sarah & Barnes Chris P. & Stumpf Michael P.H. & Cornebise Julien, 2013. "On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(1), pages 87-107, March.
    10. Brenda N Vo & Christopher C Drovandi & Anthony N Pettitt & Graeme J Pettet, 2015. "Melanoma Cell Colony Expansion Parameters Revealed by Approximate Bayesian Computation," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-22, December.
    11. Xing Ju Lee & Christopher C. Drovandi & Anthony N. Pettitt, 2015. "Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets," Biometrics, The International Biometric Society, vol. 71(1), pages 198-207, March.
    12. Nakagome Shigeki & Fukumizu Kenji & Mano Shuhei, 2013. "Kernel approximate Bayesian computation in population genetic inferences," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(6), pages 667-678, December.
    13. Chen, C.C.-M. & Drovandi, C.C. & Keith, J.M. & Anthony, K. & Caley, M.J. & Mengersen, K.L., 2017. "Bayesian semi-individual based model with approximate Bayesian computation for parameters calibration: Modelling Crown-of-Thorns populations on the Great Barrier Reef," Ecological Modelling, Elsevier, vol. 364(C), pages 113-123.
    14. Maxime Lenormand & Franck Jabot & Guillaume Deffuant, 2013. "Adaptive approximate Bayesian computation for complex models," Computational Statistics, Springer, vol. 28(6), pages 2777-2796, December.
    15. Drovandi, Christopher C. & Pettitt, Anthony N., 2011. "Likelihood-free Bayesian estimation of multivariate quantile distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2541-2556, September.
    16. Creel, Michael & Kristensen, Dennis, 2016. "On selection of statistics for approximate Bayesian computing (or the method of simulated moments)," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 99-114.
    17. R Zachariah Aandahl & Josephine F Reyes & Scott A Sisson & Mark M Tanaka, 2012. "A Model-Based Bayesian Estimation of the Rate of Evolution of VNTR Loci in Mycobacterium tuberculosis," PLOS Computational Biology, Public Library of Science, vol. 8(6), pages 1-9, June.
    18. Li, J. & Nott, D.J. & Fan, Y. & Sisson, S.A., 2017. "Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 77-89.
    19. Hai‐Dang Dau & Nicolas Chopin, 2022. "Waste‐free sequential Monte Carlo," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 114-148, February.
    20. Warne, David J. & Baker, Ruth E. & Simpson, Matthew J., 2018. "Multilevel rejection sampling for approximate Bayesian computation," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 71-86.
    21. Lee, Xing Ju & Hainy, Markus & McKeone, James P. & Drovandi, Christopher C. & Pettitt, Anthony N., 2018. "ABC model selection for spatial extremes models applied to South Australian maximum temperature data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 128-144.
    22. Hazra, Indranil & Pandey, Mahesh D. & Manzana, Noldainerick, 2020. "Approximate Bayesian computation (ABC) method for estimating parameters of the gamma process using noisy data," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    23. Lorenzo Pacchiardi & Pierre Künzli & Marcel Schöngens & Bastien Chopard & Ritabrata Dutta, 2021. "Distance-learning For Approximate Bayesian Computation To Model a Volcanic Eruption," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 288-317, May.
    24. Hasegawa, Takanori & Niida, Atsushi & Mori, Tomoya & Shimamura, Teppei & Yamaguchi, Rui & Miyano, Satoru & Akutsu, Tatsuya & Imoto, Seiya, 2016. "A likelihood-free filtering method via approximate Bayesian computation in evaluating biological simulation models," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 63-74.

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