2020/04/04 by Nick Kloodt, Kloodt, Nick
Mathematics · #FOS: Mathematics #Statistical Distribution Estimation and Applications #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2004.01923
openalex publication_date 2020/04/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Completely nonparametric transformation models with heteroscedastic errors are considered. Despite their flexibility, such models have rarely been used so far, since estimators of the model components have been missing and even identification of such models has not been clear until very recently. The results of Kloodt (2020) are used to construct the first two estimators of the transformation function in these models. While the first estimator converges to the true transformation function at a parametric rate, the second estimator can be obtained by an explicit formula and is less computationally demanding. Finally, a simulation study is followed by some concluding remarks. Assumptions and proofs can be found in the appendix.