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Approximate Bayesian Computation: A Nonparametric Perspective

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  • Blum, Michael G. B.

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  • Blum, Michael G. B., 2010. "Approximate Bayesian Computation: A Nonparametric Perspective," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1178-1187.
    • Handle: RePEc:bes:jnlasa:v:105:i:491:y:2010:p:1178-1187
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    Cited by:

    1. Soubeyrand, Samuel & Haon-Lasportes, Emilie, 2015. "Weak convergence of posteriors conditional on maximum pseudo-likelihood estimates and implications in ABC," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 84-92.
    2. Pierre-Olivier Goffard & Patrick Laub, 2021. "Approximate Bayesian Computations to fit and compare insurance loss models," Post-Print hal-02891046, HAL.
    3. Valerio Scalone, 2018. "Estimating Non-Linear DSGEs with the Approximate Bayesian Computation: an application to the Zero Lower Bound," Working papers 688, Banque de France.
    4. Pierre-Olivier Goffard & Patrick Laub, 2021. "Approximate Bayesian Computations to fit and compare insurance loss models," Working Papers hal-02891046, HAL.
    5. Goffard, Pierre-Olivier & Laub, Patrick J., 2021. "Approximate Bayesian Computations to fit and compare insurance loss models," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 350-371.
    6. Frazier, David T. & Maneesoonthorn, Worapree & Martin, Gael M. & McCabe, Brendan P.M., 2019. "Approximate Bayesian forecasting," International Journal of Forecasting, Elsevier, vol. 35(2), pages 521-539.
    7. Gareth W. Peters & Efstathios Panayi & Francois Septier, 2015. "SMC-ABC methods for the estimation of stochastic simulation models of the limit order book," Papers 1504.05806, arXiv.org.
    8. Chopin, Nicolas & Gadat, Sébastien & Guedj, Benjamin & Guyader, Arnaud & Vernet, Elodie, 2015. "On some recent advances in high dimensional Bayesian Statistics," TSE Working Papers 15-557, Toulouse School of Economics (TSE).
    9. 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.
    10. D.T. Frazier & G.M. Martin & C.P. Robert & J. Rousseau, 2016. "Asymptotic Properties of Approximate Bayesian Computation," Monash Econometrics and Business Statistics Working Papers 18/16, Monash University, Department of Econometrics and Business Statistics.
    11. Luis Alvarez & Cristine Pinto & Vladimir Ponczek, 2022. "Homophily in preferences or meetings? Identifying and estimating an iterative network formation model," Papers 2201.06694, arXiv.org, revised Mar 2024.
    12. Tatiana Dmitrieva & Kristin McCullough & Nader Ebrahimi, 2021. "Improved approximate Bayesian computation methods via empirical likelihood," Computational Statistics, Springer, vol. 36(2), pages 1533-1552, June.
    13. Rodrigues, G.S. & Nott, David J. & Sisson, S.A., 2016. "Functional regression approximate Bayesian computation for Gaussian process density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 229-241.
    14. 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.
    15. Alexander Buchholz & Nicolas CHOPIN, 2017. "Improving approximate Bayesian computation via quasi Monte Carlo," Working Papers 2017-37, Center for Research in Economics and Statistics.
    16. Gael M. Martin & Brendan P.M. McCabe & David T. Frazier & Worapree Maneesoonthorn & Christian P. Robert, 2016. "Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models," Monash Econometrics and Business Statistics Working Papers 09/16, Monash University, Department of Econometrics and Business Statistics.
    17. Kristin McCullough & Tatiana Dmitrieva & Nader Ebrahimi, 2022. "New approximate Bayesian computation algorithm for censored data," Computational Statistics, Springer, vol. 37(3), pages 1369-1397, July.
    18. repec:bla:istatr:v:83:y:2015:i:3:p:405-435 is not listed on IDEAS
    19. David T. Frazier & Gael M. Martin & Christian P. Robert, 2015. "On Consistency of Approximate Bayesian Computation," Monash Econometrics and Business Statistics Working Papers 19/15, Monash University, Department of Econometrics and Business Statistics.
    20. Golchi, Shirin & Campbell, David A., 2016. "Sequentially Constrained Monte Carlo," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 98-113.
    21. Mikael Sunnåker & Alberto Giovanni Busetto & Elina Numminen & Jukka Corander & Matthieu Foll & Christophe Dessimoz, 2013. "Approximate Bayesian Computation," PLOS Computational Biology, Public Library of Science, vol. 9(1), pages 1-10, January.
    22. Ernesto Carrella & Richard Bailey & Jens Koed Madsen, 2020. "Calibrating Agent-Based Models with Linear Regressions," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(1), pages 1-7.
    23. Thomas A. Dean & Sumeetpal S. Singh & Ajay Jasra & Gareth W. Peters, 2014. "Parameter Estimation for Hidden Markov Models with Intractable Likelihoods," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 970-987, December.
    24. Menéndez, P. & Fan, Y. & Garthwaite, P.H. & Sisson, S.A., 2014. "Simultaneous adjustment of bias and coverage probabilities for confidence intervals," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 35-44.

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