Semiparametric structural analysis in regression estimation

我们开发了基于多尺度比较和重采样技术的结构回归分析新方法。所得结果解决了在现代有限样本框架下所提出方法的最优性和效率问题。一个重要的问题是所提出的技术对不同类别回归模型的灵活性和适用性：它必须包括复杂的分类数据，非齐次，单侧或相关误差分布，均值和分位数回归等情况。该方法和结果将推广到回归模型中未知变换的情况。

We develop novel methods of structural regression analysis based on multi-scale comparison and resamplingtechniques. The obtained results address the questions of optimality and efficiency of theproposed methods in the modern finite sample framework. An important issue is the flexibility andapplicability of the proposed techniques to different classes of regression models: it has to includethe cases of complicated categorical data, inhomogeneous, one-sided or dependent error distributions,mean and quantile regression, etc. The methods and the results will be extended to the caseof unknown transformation in a regression model.