This paper addresses the deconvolution problem of estimating a square-integrable probability density from observations contaminated with additive measurement errors having a known density. The estimator begins with a density estimate of the contaminated observations and minimizes a reconstruction error penalized by an integrated squared $m$-th derivative. Theory for deconvolution has mainly focused on kernel- or wavelet-based techniques, but other methods including spline-based techniques and this smoothness-penalized estimator have been found to outperform kernel methods in simulation studies. This paper fills in some of these gaps by establishing asymptotic guarantees for the smoothness-penalized approach. Consistency is established in mean integrated squared error, and rates of convergence are derived for Gaussian, Cauchy, and Laplace error densities, attaining some lower bounds already in the literature. The assumptions are weak for most results; the estimator can be used with a broader class of error densities than the deconvoluting kernel. Our application example estimates the density of the mean cytotoxicity of certain bacterial isolates under random sampling; this mean cytotoxicity can only be measured experimentally with additive error, leading to the deconvolution problem. We also describe a method for approximating the solution by a cubic spline, which reduces to a quadratic program.
翻译:本文处理从已知密度的添加测量错误污染的观测中估计可折成数的概率密度的分解问题。 估计点以受污染的观测的密度估计开始, 并尽量减少受一个集成正方美元衍生物惩罚的重建错误。 调分理论主要侧重于内核或波盘基技术, 但其他方法, 包括基于螺旋的技术和这种平滑的平滑性分布式估测仪, 在模拟研究中发现, 超越内核的偏差密度方法。 本文填补了其中一些空白, 为平滑- 平滑化方法建立无症状保证。 固度在平均的成形错误中确立, 聚合率是高斯、 康奇和拉贝特错误密度的推导法, 在文献中已经达到一些较低的界限。 假设对于多数结果来说是薄弱的; 估计值可以使用比解相交错内核内核部分更宽的内核内核分解密度方法。 我们的应用实例估计了某种平均的稳度分辨率的密度, 在某种实验性摄取性摄取性的方法下,, 只能通过一种测量的递解性递解性递解方法来测量到某种递解的递解的递解的递解的递解的沉变的沉变的沉性的方法。