IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v58y1998i1p7-15.html
   My bibliography  Save this article

On bootstrap and analytical bias corrections

Author

Listed:
  • Ferrari, Silvia L. P.
  • Cribari-Neto, Francisco

Abstract

No abstract is available for this item.

Suggested Citation

  • Ferrari, Silvia L. P. & Cribari-Neto, Francisco, 1998. "On bootstrap and analytical bias corrections," Economics Letters, Elsevier, vol. 58(1), pages 7-15, January.
  • Handle: RePEc:eee:ecolet:v:58:y:1998:i:1:p:7-15
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165-1765(97)00276-0
    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. Ferrari, Silvia L. P. & Botter, Denise A. & Cordeiro, Gauss M. & Cribari-Neto, Francisco, 1996. "Second- and third-order bias reduction for one-parameter family models," Statistics & Probability Letters, Elsevier, vol. 30(4), pages 339-345, November.
    2. Cordeiro, Gauss M. & Klein, Ruben, 1994. "Bias correction in ARMA models," Statistics & Probability Letters, Elsevier, vol. 19(3), pages 169-176, February.
    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. Francisco Cribari-Neto & Maria Lima, 2010. "Sequences of bias-adjusted covariance matrix estimators under heteroskedasticity of unknown form," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(6), pages 1053-1082, December.
    2. Ospina, Raydonal & Cribari-Neto, Francisco & Vasconcellos, Klaus L.P., 2006. "Improved point and interval estimation for a beta regression model," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 960-981, November.
    3. Cribari-Neto, Francisco & Frery, Alejandro C. & Silva, Michel F., 2002. "Improved estimation of clutter properties in speckled imagery," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 801-824, 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. Reinsel, Gregory C. & Cheang, Wai-Kwong, 2003. "Approximate ML and REML estimation for regression models with spatial or time series AR(1) noise," Statistics & Probability Letters, Elsevier, vol. 62(2), pages 123-135, April.
    2. Mahdi Teimouri, 2022. "bccp: an R package for life-testing and survival analysis," Computational Statistics, Springer, vol. 37(1), pages 469-489, March.
    3. Yong Bao, 2015. "Should We Demean the Data?," Annals of Economics and Finance, Society for AEF, vol. 16(1), pages 163-171, May.
    4. Stelios Arvanitis & Antonis Demos, 2015. "A class of indirect inference estimators: higher‐order asymptotics and approximate bias correction," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 200-241, June.
    5. Cordeiro, Gauss M. & Vasconcellos, Klaus L. P., 1997. "Bias correction for a class of multivariate nonlinear regression models," Statistics & Probability Letters, Elsevier, vol. 35(2), pages 155-164, September.
    6. David E. Giles, 2012. "A Note on Improved Estimation for the Topp-Leone Distribution," Econometrics Working Papers 1203, Department of Economics, University of Victoria.
    7. F. Cribari-Neto & G.M. Cordeiro, 1995. "On Bartlett and Bartlett-Type Corrections," Econometrics 9507001, University Library of Munich, Germany.
    8. Cordeiro, Gauss M., 2008. "Corrected Maximum Likelihood Estimators in Linear Heteroskedastic Regression Models," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 28(1), May.
    9. Tzong-Ru Tsai & Hua Xin & Ya-Yen Fan & Yuhlong Lio, 2022. "Bias-Corrected Maximum Likelihood Estimation and Bayesian Inference for the Process Performance Index Using Inverse Gaussian Distribution," Stats, MDPI, vol. 5(4), pages 1-18, November.
    10. Xiao Ling & David E. Giles, 2014. "Bias Reduction for the Maximum Likelihood Estimator of the Parameters of the Generalized Rayleigh Family of Distributions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(8), pages 1778-1792, April.
    11. Geoffrey Decrouez & Andrew Robinson, 2018. "Bias‐Corrected Estimation in Continuous Sampling Plans," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 177-193, January.
    12. Patrick Richard, 2009. "Improving the accuracy of the analytical indirect inference estimator for MA models," Economics Bulletin, AccessEcon, vol. 29(4), pages 2795-2802.
    13. David E. Giles, 2009. "Bias Reduction for the Maximum Likelihood Estimator of the Scale Parameter in the Half-Logistic Distribution," Econometrics Working Papers 0901, Department of Economics, University of Victoria.
    14. Patriota, Alexandre G. & Lemonte, Artur J., 2009. "Bias correction in a multivariate normal regression model with general parameterization," Statistics & Probability Letters, Elsevier, vol. 79(15), pages 1655-1662, August.
    15. Demos Antonis & Kyriakopoulou Dimitra, 2019. "Finite-Sample Theory and Bias Correction of Maximum Likelihood Estimators in the EGARCH Model," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-20, January.
    16. Joseph Reath & Jianping Dong & Min Wang, 2018. "Improved parameter estimation of the log-logistic distribution with applications," Computational Statistics, Springer, vol. 33(1), pages 339-356, March.
    17. Musonda, Patrick & Hocine, Mounia N. & Whitaker, Heather J. & Farrington, C. Paddy, 2008. "Self-controlled case series analyses: Small-sample performance," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1942-1957, January.
    18. Ghitany, M.E. & Al-Mutairi, D.K. & Balakrishnan, N. & Al-Enezi, L.J., 2013. "Power Lindley distribution and associated inference," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 20-33.
    19. Gauss Cordeiro & Denise Botter & Alexsandro Cavalcanti & Lúcia Barroso, 2014. "Covariance matrix of the bias-corrected maximum likelihood estimator in generalized linear models," Statistical Papers, Springer, vol. 55(3), pages 643-652, August.
    20. Mentz, R. P. & Morettin, P. A. & Toloi, C. M. C., 1999. "On least-squares estimation of the residual variance in the first-order moving average model," Computational Statistics & Data Analysis, Elsevier, vol. 29(4), pages 485-499, February.

    More about this item

    Statistics

    Access and download statistics

    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:ecolet:v:58:y:1998:i:1:p:7-15. 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/locate/ecolet .

    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.