IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v71y2005i2p143-153.html
   My bibliography  Save this article

Parametric bootstrapping with nuisance parameters

Author

Listed:
  • Lee, Stephen M.S.
  • Young, G. Alastair

Abstract

Bootstrap methods are attractive empirical procedures for assessment of errors in problems of statistical estimation, and allow highly accurate inference in a vast range of parametric problems. Conventional parametric bootstrapping involves sampling from a fitted parametric model, obtained by substituting the maximum likelihood estimator for the unknown population parameter. Recently, attention has focussed on modified bootstrap methods which alter the sampling model used in the bootstrap calculation, in a systematic way that is dependent on the parameter of interest. Typically, inference is required for the interest parameter in the presence of a nuisance parameter, in which case the issue of how best to handle the nuisance parameter in the bootstrap inference arises. In this paper, we provide a general analysis of the error reduction properties of the parametric bootstrap. We show that conventional parametric bootstrapping succeeds in reducing error quite generally, when applied to an asymptotically normal pivot, and demonstrate further that systematic improvements are obtained by a particular form of modified scheme, in which the nuisance parameter is substituted by its constrained maximum likelihood estimator, for a given value of the parameter of interest.

Suggested Citation

  • Lee, Stephen M.S. & Young, G. Alastair, 2005. "Parametric bootstrapping with nuisance parameters," Statistics & Probability Letters, Elsevier, vol. 71(2), pages 143-153, February.
  • Handle: RePEc:eee:stapro:v:71:y:2005:i:2:p:143-153
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(04)00289-5
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Paul H. Garthwaite & Stephen T. Buckland, 1992. "Generating Monte Carlo Confidence Intervals by the Robbins–Monro Process," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 159-171, March.
    2. J. Carpenter, 1999. "Test inversion bootstrap confidence intervals," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 159-172.
    3. Stephen M. S. Lee, 2003. "Prepivoting by weighted bootstrap iteration," Biometrika, Biometrika Trust, vol. 90(2), pages 393-410, June.
    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. Robert L. Paige & A. Alexandre Trindade & P. Harshini Fernando, 2009. "Saddlepoint‐Based Bootstrap Inference for Quadratic Estimating Equations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 98-111, March.
    2. Godfrey, L.G., 2007. "Alternative approaches to implementing Lagrange multiplier tests for serial correlation in dynamic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3282-3295, April.
    3. Lloyd, Chris J., 2012. "Computing highly accurate or exact P-values using importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1784-1794.
    4. Guogen Shan & Changxing Ma, 2014. "Efficient tests for one sample correlated binary data with applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 175-188, June.
    5. Lu, H.Y. Kevin & Young, G. Alastair, 2012. "Parametric bootstrap under model mis-specification," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2410-2420.
    6. Di Caterina, Claudia & Kosmidis, Ioannis, 2019. "Location-adjusted Wald statistics for scalar parameters," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 126-142.
    7. Lloyd, Chris J., 2010. "How close are alternative bootstrap P-values?," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1972-1976, December.
    8. Chris J. Lloyd, 2010. "Bootstrap and Second-Order Tests of Risk Difference," Biometrics, The International Biometric Society, vol. 66(3), pages 975-982, September.
    9. Lloyd, Chris J., 2013. "A numerical investigation of the accuracy of parametric bootstrap for discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 1-6.

    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. G. Alastair Young, 2003. "Better bootstrapping by constrained prepivoting," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 227-242.
    2. Bruce E. Hansen, 1999. "The Grid Bootstrap And The Autoregressive Model," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 594-607, November.
    3. Gatta, Valerio & Marcucci, Edoardo & Scaccia, Luisa, 2015. "On finite sample performance of confidence intervals methods for willingness to pay measures," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 169-192.
    4. 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.
    5. Davidson, Russell, 2017. "A discrete model for bootstrap iteration," Journal of Econometrics, Elsevier, vol. 201(2), pages 228-236.
    6. (Yale) Gong, Yeming & Yücesan, Enver, 2012. "Stochastic optimization for transshipment problems with positive replenishment lead times," International Journal of Production Economics, Elsevier, vol. 135(1), pages 61-72.
    7. Hidalgo, Javier & Lee, Jungyoon & Seo, Myung Hwan, 2019. "Robust inference for threshold regression models," Journal of Econometrics, Elsevier, vol. 210(2), pages 291-309.
    8. Magnar Lillegard & Steinar Engen, 1999. "Exact confidence intervals generated by conditional parametric bootstrapping," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(4), pages 447-459.
    9. Lu, H.Y. Kevin & Young, G. Alastair, 2012. "Parametric bootstrap under model mis-specification," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2410-2420.
    10. Hristos Tyralis & Demetris Koutsoyiannis & Stefanos Kozanis, 2013. "An algorithm to construct Monte Carlo confidence intervals for an arbitrary function of probability distribution parameters," Computational Statistics, Springer, vol. 28(4), pages 1501-1527, August.
    11. van Giersbergen, Noud P. A. & Kiviet, Jan F., 2002. "How to implement the bootstrap in static or stable dynamic regression models: test statistic versus confidence region approach," Journal of Econometrics, Elsevier, vol. 108(1), pages 133-156, May.
    12. Peter Hall & D. M. Titterington & Jing‐Hao Xue, 2009. "Tilting methods for assessing the influence of components in a classifier," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 783-803, September.

    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:71:y:2005:i:2:p:143-153. 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.