A uniform central limit theorem and efficiency for deconvolution estimators

作者：Soehl Jakob; Trabs Mathias

来源：Electronic Journal of Statistics, 2012, 6: 2486-2518.

DOI：10.1214/12-EJS757

• Deconvolution
• Donsker theorem
• efficiency
• distribution function
• smoothed empirical processes
• Fourier multipliers

We estimate linear functionals in the classical deconvolution problem by kernel estimators. We obtain a uniform central limit theorem with root nrate on the assumption that the smoothness of the functionals is larger than the illposedness of the problem, which is given by the polynomial decay rate of the characteristic function of the error. The limit distribution is a generalized Brownian bridge with a covariance structure that depends on the characteristic function of the error and on the functionals. The proposed estimators are optimal in the sense of semiparametric efficiency. The class of linear functionals is wide enough to incorporate the estimation of distribution functions. The proofs are based on smoothed empirical processes and mapping properties of the deconvolution operator.

• 出版日期2012