IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v130y2017icp5-11.html
   My bibliography  Save this article

Bayesian sieve method for piece-wise smooth regression

Author

Listed:
  • Yi, Taihe
  • Wang, Zhengming

Abstract

We study the piece-wise smooth regression from a theoretical Bayesian perspective. Our results indicate that under some mild assumptions, the posterior of the regression model and the change-points locations contracts at optimal nonparametric convergence rate up to a log-factor, and the number of change-points is posterior consistent.

Suggested Citation

  • Yi, Taihe & Wang, Zhengming, 2017. "Bayesian sieve method for piece-wise smooth regression," Statistics & Probability Letters, Elsevier, vol. 130(C), pages 5-11.
    • Handle: RePEc:eee:stapro:v:130:y:2017:i:c:p:5-11
    DOI: 10.1016/j.spl.2017.07.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715217302389
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2017.07.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. repec:dau:papers:123456789/11426 is not listed on IDEAS
    2. Jeng, X. Jessie & Cai, T. Tony & Li, Hongzhe, 2010. "Optimal Sparse Segment Identification With Application in Copy Number Variation Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1156-1166.
    3. Gilles Teyssière & Alan P. Kirman (ed.), 2007. "Long Memory in Economics," Springer Books, Springer, number 978-3-540-34625-8, December.
    4. Weining Shen & Subhashis Ghosal, 2015. "Adaptive Bayesian Procedures Using Random Series Priors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1194-1213, December.
    5. Chao Du & Chu-Lan Michael Kao & S. C. Kou, 2016. "Stepwise Signal Extraction via Marginal Likelihood," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 314-330, March.
    6. Julyan Arbel & Ghislaine Gayraud & Judith Rousseau, 2013. "Bayesian Optimal Adaptive Estimation Using a Sieve Prior," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 549-570, September.
    7. Julyan Arbel & Ghislaine Gayraud & Judith Rousseau, 2013. "Bayesian Optimal Adaptive Estimation Using a Sieve prior," Working Papers 2013-19, Center for Research in Economics and Statistics.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. van Waaij, Jan & van Zanten, Harry, 2017. "Full adaptation to smoothness using randomly truncated series priors with Gaussian coefficients and inverse gamma scaling," Statistics & Probability Letters, Elsevier, vol. 123(C), pages 93-99.
    2. Weining Shen & Subhashis Ghosal, 2015. "Adaptive Bayesian Procedures Using Random Series Priors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1194-1213, December.
    3. Florian Pein & Hannes Sieling & Axel Munk, 2017. "Heterogeneous change point inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1207-1227, September.
    4. I. Votsi & G. Gayraud & V. S. Barbu & N. Limnios, 2021. "Hypotheses testing and posterior concentration rates for semi-Markov processes," Statistical Inference for Stochastic Processes, Springer, vol. 24(3), pages 707-732, October.
    5. Jan Johannes & Anna Simoni & Rudolf Schenk, 2020. "Adaptive Bayesian Estimation in Indirect Gaussian Sequence Space Models," Annals of Economics and Statistics, GENES, issue 137, pages 83-116.
    6. Zhe Wang & Ryan Martin, 2021. "Gibbs posterior inference on a Levy density under discrete sampling," Papers 2109.06567, arXiv.org.
    7. Qianwen Tan & Subhashis Ghosal, 2021. "Bayesian Analysis of Mixed-effect Regression Models Driven by Ordinary Differential Equations," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 3-29, May.
    8. Scheffknecht, Lukas & Geiger, Felix, 2011. "A behavioral macroeconomic model with endogenous boom-bust cycles and leverage dynamcis," FZID Discussion Papers 37-2011, University of Hohenheim, Center for Research on Innovation and Services (FZID).
    9. Anufriev, Mikhail & Panchenko, Valentyn, 2009. "Asset prices, traders' behavior and market design," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1073-1090, May.
    10. Kei Nanamiya, 2011. "The Wavelet-based Estimation for Long Memory Signal Plus Noise Models," Global COE Hi-Stat Discussion Paper Series gd11-210, Institute of Economic Research, Hitotsubashi University.
    11. Wu Wang & Xuming He & Zhongyi Zhu, 2020. "Statistical inference for multiple change‐point models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1149-1170, December.
    12. Staccioli, Jacopo & Napoletano, Mauro, 2021. "An agent-based model of intra-day financial markets dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 331-348.
    13. Franke, Reiner, 2008. "Artificial Long Memory Effects in Two Agend-Based Asset Pricing Models," Economics Working Papers 2008-15, Christian-Albrechts-University of Kiel, Department of Economics.
    14. Schmitt, Noemi & Westerhoff, Frank, 2014. "Speculative behavior and the dynamics of interacting stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 262-288.
    15. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2011. "Do heterogeneous beliefs diversify market risk?," The European Journal of Finance, Taylor & Francis Journals, vol. 17(3), pages 241-258.
    16. Turhan, M. Ibrahim & Sensoy, Ahmet & Ozturk, Kevser & Hacihasanoglu, Erk, 2014. "A view to the long-run dynamic relationship between crude oil and the major asset classes," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 286-299.
    17. Franke, Reiner, 2010. "On the specification of noise in two agent-based asset pricing models," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1140-1152, June.
    18. Guy P. Nason & Ben Powell & Duncan Elliott & Paul A. Smith, 2017. "Should we sample a time series more frequently?: decision support via multirate spectrum estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 353-407, February.
    19. He, Xue-Zhong & Li, Kai, 2012. "Heterogeneous beliefs and adaptive behaviour in a continuous-time asset price model," Journal of Economic Dynamics and Control, Elsevier, vol. 36(7), pages 973-987.
    20. Dieci, Roberto & Westerhoff, Frank, 2016. "Heterogeneous expectations, boom-bust housing cycles, and supply conditions: A nonlinear economic dynamics approach," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 21-44.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:130:y:2017:i:c:p:5-11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.