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Data‐driven density estimation in the presence of additive noise with unknown distribution

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  • F. Comte
  • C. Lacour

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  • F. Comte & C. Lacour, 2011. "Data‐driven density estimation in the presence of additive noise with unknown distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 601-627, September.
    • Handle: RePEc:bla:jorssb:v:73:y:2011:i:4:p:601-627
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    Cited by:

    1. Christophe Chesneau & Fabienne Comte & Gwennaëlle Mabon & Fabien Navarro, 2014. "Estimation of Convolution In The Model with Noise," Working Papers 2014-39, Center for Research in Economics and Statistics.
    2. Katerina Papagiannouli, 2022. "A Lepskiĭ-type stopping rule for the covariance estimation of multi-dimensional Lévy processes," Statistical Inference for Stochastic Processes, Springer, vol. 25(3), pages 505-535, October.
    3. Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.
    4. Van Ha Hoang & Thanh Mai Pham Ngoc & Vincent Rivoirard & Viet Chi Tran, 2022. "Nonparametric estimation of the fragmentation kernel based on a partial differential equation stationary distribution approximation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 4-43, March.
    5. Johanna Kappus, 2012. "Nonparametric adaptive estimation of linear functionals for low frequency observed Lévy processes," SFB 649 Discussion Papers SFB649DP2012-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Kappus, Johanna, 2014. "Adaptive nonparametric estimation for Lévy processes observed at low frequency," Stochastic Processes and their Applications, Elsevier, vol. 124(1), pages 730-758.
    7. 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.
    8. Hao Dong & Yuya Sasaki, 2022. "Estimation of average derivatives of latent regressors: with an application to inference on buffer-stock saving," Departmental Working Papers 2204, Southern Methodist University, Department of Economics.
    9. Fabienne Comte & Adeline Samson & Julien J Stirnemann, 2014. "Deconvolution Estimation of Onset of Pregnancy with Replicate Observations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 325-345, June.
    10. Fabienne Comte & Adeline Samson, 2012. "Nonparametric estimation of random-effects densities in linear mixed-effects model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 951-975, December.
    11. Kato, Kengo & Sasaki, Yuya, 2018. "Uniform confidence bands in deconvolution with unknown error distribution," Journal of Econometrics, Elsevier, vol. 207(1), pages 129-161.
    12. Gautier, Eric & Hoderlein, Stefan, 2011. "A triangular treatment effect model with random coefficients in the selection equation," TSE Working Papers 15-598, Toulouse School of Economics (TSE), revised 25 Aug 2015.
    13. Martin Kroll, 2019. "Nonparametric intensity estimation from noisy observations of a Poisson process under unknown error distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(8), pages 961-990, November.
    14. Doruk Cengiz & Arindrajit Dube & Attila Lindner & Ben Zipperer, 2018. "The effect of minimum wages on low-wage jobs: evidence from the United States using a bunching estimator," CEP Discussion Papers dp1531, Centre for Economic Performance, LSE.
    15. Gwennaëlle Mabon, 2014. "Adaptive Estimation of Random-Effects Densities In Linear Mixed-Effects Model," Working Papers 2014-41, Center for Research in Economics and Statistics.
    16. Wang, Xiao-Feng & Ye, Deping, 2015. "Conditional density estimation in measurement error problems," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 38-50.
    17. Dattner, I., 2013. "Deconvolution of P(X," Statistics & Probability Letters, Elsevier, vol. 83(8), pages 1880-1887.
    18. Johanna Kappus & Gwennaelle Mabon, 2013. "Adaptive Density Estimation in Deconvolution Problems with Unknown Error Distribution," Working Papers 2013-31, Center for Research in Economics and Statistics.
    19. Eric Gautier & Stefan Hoderlein, 2012. "A triangular treatment effect model with random coefficients in the selection equation," CeMMAP working papers 39/12, Institute for Fiscal Studies.
    20. Guillermo Basulto-Elias & Alicia L. Carriquiry & Kris Brabanter & Daniel J. Nordman, 2021. "Bivariate Kernel Deconvolution with Panel Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 122-151, May.
    21. Gwennaëlle Mabon, 2014. "Adaptive Deconvolution on the Nonnegative Real Line," Working Papers 2014-40, Center for Research in Economics and Statistics.
    22. Kengo Kato & Yuya Sasaki & Takuya Ura, 2018. "Inference based on Kotlarski's Identity," Papers 1808.09375, arXiv.org, revised Sep 2019.

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