Efficient nonparametric regression when the support is bounded

点击翻译按钮获取中文摘要

If in nonparametric regression the support of the error distribution has a sharp boundary, then the regression function and functionals thereof can be estimated with a higher rate of convergence than in regular models. We examine the geometry of such irregular statistical experiments and develop efficient statistical procedures that adapt both to the smoothness of the regression functionand to the degree of irregularity of the error distribution. Moreover, goodness-of-fit tests for the model assumptions will be constructed and concrete estimation procedures for order-book data will be developed.