IDEAS home Printed from https://ideas.repec.org/a/spr/sistpr/v4y2001i1p53-71.html
   My bibliography  Save this article

Modeling and Smoothing Unequally Spaced Sequence Data

Author

Listed:
  • Piet Jong
  • Sonia Mazzi

Abstract

No abstract is available for this item.

Suggested Citation

  • Piet Jong & Sonia Mazzi, 2001. "Modeling and Smoothing Unequally Spaced Sequence Data," Statistical Inference for Stochastic Processes, Springer, vol. 4(1), pages 53-71, January.
    • Handle: RePEc:spr:sistpr:v:4:y:2001:i:1:p:53-71
    DOI: 10.1023/A:1017510420686
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1017510420686
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1017510420686?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. D. G. T. Denison & B. K. Mallick & A. F. M. Smith, 1998. "Automatic Bayesian curve fitting," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 333-350.
    2. Lavielle, Marc, 1999. "Detection of multiple changes in a sequence of dependent variables," Stochastic Processes and their Applications, Elsevier, vol. 83(1), pages 79-102, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rob J Hyndman & Maxwell L. King & Ivet Pitrun & Baki Billah, 2002. "Local Linear Forecasts Using Cubic Smoothing Splines," Monash Econometrics and Business Statistics Working Papers 10/02, Monash University, Department of Econometrics and Business Statistics.
    2. Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 935-958, October.

    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. Surgailis, Donatas & Teyssière, Gilles & Vaiciulis, Marijus, 2008. "The increment ratio statistic," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 510-541, March.
    2. Salvatore Fasola & Vito M. R. Muggeo & Helmut Küchenhoff, 2018. "A heuristic, iterative algorithm for change-point detection in abrupt change models," Computational Statistics, Springer, vol. 33(2), pages 997-1015, June.
    3. Gianluca Frasso & Jonathan Jaeger & Philippe Lambert, 2016. "Parameter estimation and inference in dynamic systems described by linear partial differential equations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(3), pages 259-287, July.
    4. Fontaine, Charles & Frostig, Ron D. & Ombao, Hernando, 2020. "Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials," Econometrics and Statistics, Elsevier, vol. 15(C), pages 85-103.
    5. M. P. Wand, 2000. "A Comparison of Regression Spline Smoothing Procedures," Computational Statistics, Springer, vol. 15(4), pages 443-462, December.
    6. Boracchi, Patrizia & Biganzoli, Elia & Marubini, Ettore, 2003. "Joint modelling of cause-specific hazard functions with cubic splines: an application to a large series of breast cancer patients," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 243-262, February.
    7. Mondher Bellalah & Marc Lavielle, 2002. "A Decomposition of Empirical Distributions with Applications to the Valuation of Derivative Assets," Multinational Finance Journal, Multinational Finance Journal, vol. 6(2), pages 99-130, June.
    8. Basna, Rani & Nassar, Hiba & Podgórski, Krzysztof, 2022. "Data driven orthogonal basis selection for functional data analysis," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    9. Cardot, Hervé, 2002. "Spatially Adaptive Splines for Statistical Linear Inverse Problems," Journal of Multivariate Analysis, Elsevier, vol. 81(1), pages 100-119, April.
    10. Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892, November.
    11. Elcin Koc & Cem Iyigun & İnci Batmaz & Gerhard-Wilhelm Weber, 2014. "Efficient adaptive regression spline algorithms based on mapping approach with a case study on finance," Journal of Global Optimization, Springer, vol. 60(1), pages 103-120, September.
    12. Botts, Carsten H. & Daniels, Michael J., 2008. "A flexible approach to Bayesian multiple curve fitting," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5100-5120, August.
    13. Wai-Yin Poon & Hai-Bin Wang, 2014. "Multivariate partially linear single-index models: Bayesian analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(4), pages 755-768, December.
    14. Pena, Daniel & Redondas, Dolores, 2006. "Bayesian curve estimation by model averaging," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 688-709, February.
    15. Kim, Daeju & Kawano, Shuichi & Ninomiya, Yoshiyuki, 2023. "Smoothly varying regularization," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    16. P.L. Davies & M. Meise, 2008. "Approximating data with weighted smoothing splines," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(3), pages 207-228.
    17. Ghislaine Gayraud & Judith Rousseau, 2002. "Nonparametric Bayesian Estimation of Level Sets," Working Papers 2002-03, Center for Research in Economics and Statistics.
    18. Villani, Mattias & Kohn, Robert & Giordani, Paolo, 2007. "Nonparametric Regression Density Estimation Using Smoothly Varying Normal Mixtures," Working Paper Series 211, Sveriges Riksbank (Central Bank of Sweden).
    19. Leitenstorfer, Florian & Tutz, Gerhard, 2007. "Knot selection by boosting techniques," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4605-4621, May.
    20. Cosimo Petracchi, 2021. "The Mussa Puzzle: A Generalization," Working Papers 2021-001, Brown University, Department of Economics.

    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:spr:sistpr:v:4:y:2001:i:1:p:53-71. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.