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.
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