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Density deconvolution from grouped data with additive errors

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  • Phuong, Cao Xuan
  • Thuy, Le Thi Hong

Abstract

We study the problem of estimating a density based on grouped data with additive errors. We propose a consistent estimator for the density and compute upper bounds on convergence rate of L2-risk in some cases of error density.

Suggested Citation

  • Phuong, Cao Xuan & Thuy, Le Thi Hong, 2019. "Density deconvolution from grouped data with additive errors," Statistics & Probability Letters, Elsevier, vol. 148(C), pages 74-81.
    • Handle: RePEc:eee:stapro:v:148:y:2019:i:c:p:74-81
    DOI: 10.1016/j.spl.2019.01.007
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    References listed on IDEAS

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    1. Linton, Oliver & Whang, Yoon-Jae, 2002. "Nonparametric Estimation With Aggregated Data," Econometric Theory, Cambridge University Press, vol. 18(2), pages 420-468, April.
    2. Meister, Alexander, 2007. "Optimal convergence rates for density estimation from grouped data," Statistics & Probability Letters, Elsevier, vol. 77(11), pages 1091-1097, June.
    3. Camelia Minoiu & Sanjay Reddy, 2014. "Kernel density estimation on grouped data: the case of poverty assessment," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 12(2), pages 163-189, June.
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