We consider density deconvolution with zero-mean Laplace noise in the context of an error component regression model. We adapt the minimax deconvolution methods of Meister (2006) to allow estimation of the unknown noise variance. We propose a semi-uniformly consistent estimator for an ordinary-smooth target density and a modified "variance truncation device” for the unknown noise variance. We provide a simulation study and practical guidance for the choice of smoothness parameters of the ordinary-smooth target density. We apply restricted versions of our estimator to a stochastic frontier model of US banks and to a measurement error model of daily saturated fat intake.

我们在误差分量回归模型的背景下考虑具有零均值拉普拉斯噪声的密度反卷积。我们采用了 Meister (2006) 的极小极大反卷积方法来估计未知的噪声方差。我们提出了一种用于普通光滑目标密度的半一致一致估计器和一种用于未知噪声方差的改进“方差截断装置”。我们为普通光滑目标密度的光滑度参数选择提供了模拟研究和实践指导. 我们将估计量的受限版本应用于美国银行的随机前沿模型和每日饱和脂肪摄入量的测量误差模型。