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Methodology for non-parametric deconvolution when the error distribution is unknown

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  • Aurore Delaigle
  • Peter Hall

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  • Aurore Delaigle & Peter Hall, 2016. "Methodology for non-parametric deconvolution when the error distribution is unknown," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 231-252, January.
    • Handle: RePEc:bla:jorssb:v:78:y:2016:i:1:p:231-252
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    File URL: http://hdl.handle.net/10.1111/rssb.12109
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    References listed on IDEAS

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    1. Yuedong Wang & Yanyuan Ma & Raymond J. Carroll, 2009. "Variance estimation in the analysis of microarray data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 425-445, April.
    2. Stefanski, Leonard A., 1990. "Rates of convergence of some estimators in a class of deconvolution problems," Statistics & Probability Letters, Elsevier, vol. 9(3), pages 229-235, March.
    3. Peter Hall & Yanyuan Ma, 2007. "Semiparametric estimators of functional measurement error models with unknown error," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 429-446, June.
    4. A. Delaigle & I. Gijbels, 2002. "Estimation of integrated squared density derivatives from a contaminated sample," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 869-886, October.
    5. Xihong Lin & Raymond J. Carroll, 2006. "Semiparametric estimation in general repeated measures problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 69-88, February.
    6. Delaigle, Aurore & Hall, Peter, 2008. "Using SIMEX for Smoothing-Parameter Choice in Errors-in-Variables Problems," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 280-287, March.
    7. Li, Tong & Vuong, Quang, 1998. "Nonparametric Estimation of the Measurement Error Model Using Multiple Indicators," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 139-165, May.
    8. Julie McIntyre & Leonard Stefanski, 2011. "Density Estimation with Replicate Heteroscedastic Measurements," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(1), pages 81-99, February.
    9. Ma, Yanyuan & Yin, Guosheng, 2008. "Cure Rate Model With Mismeasured Covariates Under Transformation," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 743-756, June.
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    Citations

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    Cited by:

    1. Jean-Pierre Florens & Léopold Simar & Ingrid Van Keilegom, 2020. "Estimation of the Boundary of a Variable Observed With Symmetric Error," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 425-441, January.
    2. Christian Gourieroux & Joann Jasiak, 2023. "Dynamic deconvolution and identification of independent autoregressive sources," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 151-180, March.
    3. Aurélie Bertrand & Ingrid Van Keilegom & Catherine Legrand, 2019. "Flexible parametric approach to classical measurement error variance estimation without auxiliary data," Biometrics, The International Biometric Society, vol. 75(1), pages 297-307, March.
    4. Hohage, Thorsten & Maréchal, Pierre & Simar, Léopold & Vanhems, Anne, 2024. "A Mollifier Approach To The Deconvolution Of Probability Densities," Econometric Theory, Cambridge University Press, vol. 40(2), pages 320-359, April.
    5. Christophe Chesneau & Salima El Kolei & Fabien Navarro, 2022. "Parametric estimation of hidden Markov models by least squares type estimation and deconvolution," Statistical Papers, Springer, vol. 63(5), pages 1615-1648, October.
    6. Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.
    7. Dong, Hao & Taylor, Luke, 2022. "Nonparametric Significance Testing In Measurement Error Models," Econometric Theory, Cambridge University Press, vol. 38(3), pages 454-496, June.
    8. Bertrand, Aurelie & Van Keilegom, Ingrid & Legrand, Catherine, 2017. "Flexible parametric approach to classical measurement error variance estimation without auxiliary data," LIDAM Discussion Papers ISBA 2017025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. Jun Cai & William C. Horrace & Christopher F. Parmeter, 2021. "Density deconvolution with Laplace errors and unknown variance," Journal of Productivity Analysis, Springer, vol. 56(2), pages 103-113, December.
    10. Denis Belomestny & Mathias Trabs & Alexandre Tsybakov, 2017. "Sparse covariance matrix estimation in high-dimensional deconvolution," Working Papers 2017-25, Center for Research in Economics and Statistics.
    11. Dong, Hao & Otsu, Taisuke & Taylor, Luke, 2022. "Estimation of varying coefficient models with measurement error," Journal of Econometrics, Elsevier, vol. 230(2), pages 388-415.
    12. Kato, Kengo & Sasaki, Yuya, 2018. "Uniform confidence bands in deconvolution with unknown error distribution," Journal of Econometrics, Elsevier, vol. 207(1), pages 129-161.
    13. Cornelis J. Potgieter, 2020. "Density deconvolution for generalized skew-symmetric distributions," Journal of Statistical Distributions and Applications, Springer, vol. 7(1), pages 1-20, December.

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