IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v17y2008i1p13-40.html
   My bibliography  Save this article

The cost of not knowing the radius

Author

Listed:
  • Helmut Rieder
  • Matthias Kohl
  • Peter Ruckdeschel

Abstract

No abstract is available for this item.

Suggested Citation

  • Helmut Rieder & Matthias Kohl & Peter Ruckdeschel, 2008. "The cost of not knowing the radius," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(1), pages 13-40, February.
    • Handle: RePEc:spr:stmapp:v:17:y:2008:i:1:p:13-40
    DOI: 10.1007/s10260-007-0047-7
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10260-007-0047-7
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10260-007-0047-7?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. Rudolf Beran, 1976. "Adaptive estimates for autoregressive processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 28(1), pages 77-89, December.
    2. Ruckdeschel Peter & Rieder Helmut, 2004. "Optimal influence curves for general loss functions," Statistics & Risk Modeling, De Gruyter, vol. 22(3), pages 201-223, March.
    3. Rieder, Helmut, 2000. "Neighborhoods as nuisance parameters? Robustness vs. semiparametrics," SFB 373 Discussion Papers 2000,25, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    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. Tino Werner, 2022. "Asymptotic linear expansion of regularized M-estimators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(1), pages 167-194, February.
    2. Matthias Kohl & Peter Ruckdeschel & Helmut Rieder, 2010. "Infinitesimally Robust estimation in general smoothly parametrized models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(3), pages 333-354, August.
    3. Toma, Aida & Leoni-Aubin, Samuela, 2013. "Optimal robust M-estimators using Rényi pseudodistances," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 359-373.
    4. William H. Aeberhard & Eva Cantoni & Chris Field & Hans R. Künsch & Joanna Mills Flemming & Ximing Xu, 2021. "Robust estimation for discrete‐time state space models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1127-1147, December.

    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. Potscher, Benedikt M., 1995. "Comment on 'Adaptive estimation in time series regression models' by D.G. Steigerwald," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 123-129.
    2. Haoying Wang, 2018. "Pricing used books on Amazon.com: a spatial approach to price dispersion," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(1), pages 99-117, January.
    3. Gupta, A, 2015. "Nonparametric specification testing via the trinity of tests," Economics Discussion Papers 15619, University of Essex, Department of Economics.
    4. Meintanis, S. G. & Donatos, G. S., 1999. "Finite-sample performance of alternative estimators for autoregressive models in the presence of outliers," Computational Statistics & Data Analysis, Elsevier, vol. 31(3), pages 323-339, September.
    5. Marc Hallin & Abdeslam Serroukh, 1998. "Adaptive Estimation of the Lag of a Long–memory Process," Statistical Inference for Stochastic Processes, Springer, vol. 1(2), pages 111-129, May.
    6. Robinson, P.M., 2010. "Efficient estimation of the semiparametric spatial autoregressive model," Journal of Econometrics, Elsevier, vol. 157(1), pages 6-17, July.
    7. Gupta, Abhimanyu, 2018. "Nonparametric specification testing via the trinity of tests," Journal of Econometrics, Elsevier, vol. 203(1), pages 169-185.
    8. Peter M Robinson, 2007. "Efficient Estimation of the SemiparametricSpatial Autoregressive Model," STICERD - Econometrics Paper Series 515, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    9. Tata Subba Rao & Granville Tunnicliffe Wilson & P. M. Robinson & L. Taylor, 2017. "Adaptive Estimation in Multiple Time Series With Independent Component Errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 191-203, March.
    10. Robinson, Peter, 2008. "Developments in the analysis of spatial data," LSE Research Online Documents on Economics 25473, London School of Economics and Political Science, LSE Library.
    11. Peter M Robinson, 2009. "Developments in the Analysis of Spatial Data," STICERD - Econometrics Paper Series 531, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    12. Lee, Jungyoon & Robinson, Peter M., 2020. "Adaptive inference on pure spatial models," Journal of Econometrics, Elsevier, vol. 216(2), pages 375-393.
    13. Matthias Kohl & Peter Ruckdeschel & Helmut Rieder, 2010. "Infinitesimally Robust estimation in general smoothly parametrized models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(3), pages 333-354, August.
    14. Robinson, Peter, 2004. "Efficiency improvements in inference on stationary and nonstationary fractional time series," LSE Research Online Documents on Economics 2126, London School of Economics and Political Science, LSE Library.
    15. Peter M Robinson, 2004. "Efficiency Improvements in Inference on Stationary and Nonstationary Fractional Time Series," STICERD - Econometrics Paper Series 480, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    16. Robinson, Peter M., 2007. "Efficient estimation of the semiparametric spatial autoregressive model," LSE Research Online Documents on Economics 4535, London School of Economics and Political Science, LSE Library.
    17. Peter Robinson, 2006. "Efficient estimation of the semiparametric spatial autoregressive model," CeMMAP working papers CWP08/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. repec:esx:essedp:774 is not listed on IDEAS

    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:stmapp:v:17:y:2008:i:1:p:13-40. 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.