The one-step approach to minimum distance estimation with applications

我提出的研究计划的主要目标是调查统计估计与最小距离技术。粗略地说，最小距离估计量是选择用于最小化数据与拟合模型之间的距离的参数估计量。已经观察到，最小距离估计是自动鲁棒的，这使得它们在实践中很有吸引力。文献中已经考虑了各种模型的几种最小距离估计，主要是基于分布函数。一般来说，不同的距离产生的估计量是一致的和渐近正态的。计算的可行性是任何估算程序的一个重要因素。在大多数情况下，计算的难易程度取决于距离的选择以及模型本质上是参数还是半参数。

The main objective of my proposed research program is to investigate statistical estimation with minimum distance techniques. Roughly speaking, minimum distance estimators are parameter estimates chosen to minimize a distance between the data and the fitted model. It has been observed that minimum distance estimators are automatically robust, which makes them appealing in practice. Several minimum distance estimators for various models have been considered in the literature, primarily based on distribution functions. In general, different distances produce estimators that are consistent and asymptotically normal. Computational feasibility is an important factor for any estimation procedure. In most cases, the ease of computing depends on the choice of the distance and whether the model is parametric or semi-parametric in nature.