Methods for Density Estimation in Thick-Slice Versions of Wicksell's Problem
提出一种新的隐式方法解决反问题中的密度估计，避免显式反演技术的负值问题，并给出平滑参数的经验选择方法，应用于电鳐电器官神经末梢半径数据的厚切片Wicksell问题。
Abstract A new, implicit method is suggested for density estimation in inverse problems, where data are drawn not from the target distribution, but rather from its image under a transformation. The approach that we propose produces density estimators that are themselves densities, without the negativity problems known to plague more explicit inversion techniques. We also suggest a general empirical approach to selecting the smoothing parameter so as to optimize performance in the context of the target distribution, rather than its image after the transformation. We apply the new methods, and competing techniques, to a thick-section Wicksell-type problem, using data on the radii of nerve terminals from the electric organ of the electric ray Torpedo marmorata. It is shown that statistical properties of estimators in this problem are very different from those for the thin-slice, classical Wicksell problem, and so the two cases cannot be developed simply by analogy with one another.