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Nonparametric confidence bands in deconvolution density estimation

Citations

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Cited by:

  1. Bissantz, Nicolai & Holzmann, Hajo & Proksch, Katharina, 2014. "Confidence regions for images observed under the Radon transform," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 86-107.
  2. 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.
  3. Ma, Jun & Marmer, Vadim & Yu, Zhengfei, 2023. "Inference on individual treatment effects in nonseparable triangular models," Journal of Econometrics, Elsevier, vol. 235(2), pages 2096-2124.
  4. Haoze Hou & Wei Huang & Zheng Zhang, 2025. "Non-parametric Quantile Regression and Uniform Inference with Unknown Error Distribution," Papers 2504.01761, arXiv.org.
  5. Huang, Danyang & Hu, Wei & Jing, Bingyi & Zhang, Bo, 2023. "Grouped spatial autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
  6. Irene Botosaru & James L. Powell, 2026. "Correlated Random Coefficient Distributions in Linear Panel Models," Papers 2605.21367, arXiv.org.
  7. Katharina Proksch, 2016. "On confidence bands for multivariate nonparametric regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(1), pages 209-236, February.
  8. Adusumilli, Karun & Kurisu, Daisuke & Otsu, Taisuke & Whang, Yoon-Jae, 2020. "Inference on distribution functions under measurement error," Journal of Econometrics, Elsevier, vol. 215(1), pages 131-164.
  9. Bissantz, Nicolai & Birke, Melanie, 2009. "Asymptotic normality and confidence intervals for inverse regression models with convolution-type operators," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2364-2375, November.
  10. Joel L. Horowitz, 2013. "Ill-posed inverse problems in economics," CeMMAP working papers 37/13, Institute for Fiscal Studies.
  11. Kato, Kengo & Kurisu, Daisuke, 2020. "Bootstrap confidence bands for spectral estimation of Lévy densities under high-frequency observations," Stochastic Processes and their Applications, Elsevier, vol. 130(3), pages 1159-1205.
  12. Dong, Hao & Otsu, Taisuke & Taylor, Luke, 2021. "Average Derivative Estimation Under Measurement Error," Econometric Theory, Cambridge University Press, vol. 37(5), pages 1004-1033, October.
  13. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
  14. Birke, Melanie & Bissantz, Nicolai & Holzmann, Hajo, 2008. "Confidence bands for inverse regression models with application to gel electrophoresis," Technical Reports 2008,16, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  15. Hao Dong & Daniel L. Millimet, 2020. "Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions," JRFM, MDPI, vol. 13(11), pages 1-24, November.
  16. Birke, Melanie & Bissantz, Nicolai, 2007. "Shape constrained estimators in inverse regression models with convolution-type operator," Technical Reports 2007,35, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  17. Söhl, Jakob & Trabs, Mathias, 2012. "A uniform central limit theorem and efficiency for deconvolution estimators," SFB 649 Discussion Papers 2012-046, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  18. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Anti-concentration and honest, adaptive confidence bands," CeMMAP working papers CWP69/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  19. Dong, Hao & Taylor, Luke, 2022. "Nonparametric Significance Testing In Measurement Error Models," Econometric Theory, Cambridge University Press, vol. 38(3), pages 454-496, June.
  20. Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.
  21. Bissantz, Nicolai & Birke, Melanie, 2008. "Asymptotic normality and confidence intervals for inverse regression models with convolution-type operators," Technical Reports 2008,17, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  22. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On linearization of nonparametric deconvolution estimators for repeated measurements model," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  23. repec:hum:wpaper:sfb649dp2014-028 is not listed on IDEAS
  24. Wang, Xiao-Feng & Fan, Zhaozhi & Wang, Bin, 2010. "Estimating smooth distribution function in the presence of heteroscedastic measurement errors," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 25-36, January.
  25. repec:jss:jstsof:39:i10 is not listed on IDEAS
  26. Shih-Kang Chao & Katharina Proksch & Holger Dette & Wolfgang Karl Härdle, 2017. "Confidence Corridors for Multivariate Generalized Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 70-85, January.
  27. Bengs, Viktor & Eulert, Matthias & Holzmann, Hajo, 2019. "Asymptotic confidence sets for the jump curve in bivariate regression problems," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 291-312.
  28. 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.
  29. Suigen Yang & Xiujuan Yang & Xuefei Wang, 2024. "Estimation and Simultaneous Confidence Bands for Fixed-Effects Panel Data Partially Linear Models," Mathematics, MDPI, vol. 12(23), pages 1-18, November.
  30. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On linearization of nonparametric deconvolution estimators for repeated measurements model," LSE Research Online Documents on Economics 112676, London School of Economics and Political Science, LSE Library.
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