IDEAS home Printed from https://ideas.repec.org/a/spr/testjl/v5y1996i1p125-144.html
   My bibliography  Save this article

Nonparametric conservative bands for the trend of Gaussian AR(p) models

Author

Listed:
  • R. Fraiman
  • G. Pérez-Iribarren

Abstract

No abstract is available for this item.

Suggested Citation

  • R. Fraiman & G. Pérez-Iribarren, 1996. "Nonparametric conservative bands for the trend of Gaussian AR(p) models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 5(1), pages 125-144, June.
    • Handle: RePEc:spr:testjl:v:5:y:1996:i:1:p:125-144
    DOI: 10.1007/BF02562685
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02562685
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF02562685?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. Hardle, W. & Marron, J., 1989. "Bootstrap Simultaneous Error Bars For Nonparametric Regression," LIDAM Discussion Papers CORE 1989023, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    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. Kauermann, Göran & Müller, Marlene & Carroll, Raymond J., 1998. "The efficiency of bias-corrected estimators for nonparametric kernel estimation based on local estimating equations," Statistics & Probability Letters, Elsevier, vol. 37(1), pages 41-47, January.
    2. Stefan Profit & Stefan Sperlich, 2004. "Non-uniformity of job-matching in a transition economy - A nonparametric analysis for the Czech Republic," Applied Economics, Taylor & Francis Journals, vol. 36(7), pages 695-714.
    3. Qian, Junhui & Wang, Le, 2012. "Estimating semiparametric panel data models by marginal integration," Journal of Econometrics, Elsevier, vol. 167(2), pages 483-493.
    4. Severance-Lossin, E. & Sperlich, S., 1995. "Estimation of Derivatives for Additive Separable Models," SFB 373 Discussion Papers 1995,60, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    5. Peter Hall & Joel L. Horowitz, 2012. "A simple bootstrap method for constructing nonparametric confidence bands for functions," CeMMAP working papers CWP14/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Politis, Dimitris N, 2010. "Model-free Model-fitting and Predictive Distributions," University of California at San Diego, Economics Working Paper Series qt67j6s174, Department of Economics, UC San Diego.
    7. Oliver Linton & Pedro Gozalo, 1995. "Testing Additivity in Generalized Nonparametric Regression Models," Cowles Foundation Discussion Papers 1106, Cowles Foundation for Research in Economics, Yale University.
    8. Lawrence Brown & Xin Fu & Linda Zhao, 2011. "Confidence intervals for nonparametric regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(1), pages 149-163.
    9. Bissantz, Nicolai & Dümbgen, Lutz & Munk, Axel & Stratmann, Bernd, 2008. "Convergence analysis of generalized iteratively reweighted least squares algorithms on convex function spaces," Technical Reports 2008,25, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    10. Sommerfeld, Volker, 1997. "Construction of automatic confidence intervals in nonparametric heteroscedastic regression by a moment-oriented bootstrap," SFB 373 Discussion Papers 1997,22, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    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:testjl:v:5:y:1996:i:1:p:125-144. 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.