The one-step approach to minimum distance estimation with applications

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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.