Discontinuities in robust nonparametric regression with alpha-mixing dependence

作者：Huskova Marie; Maciak Matus^*

来源：Journal of Nonparametric Statistics, 2017, 29(2): 447-475.

DOI：10.1080/10485252.2017.1303061

• Local polynomial M-smoothers
• robust nonparametric regression
• discontinuity in regression
• change points
• alpha-mixing dependence
• moving blocks bootstrap

The main idea of the paper is to introduce a robust regression estimation method under an alpha-mixing dependence assumption, staying free of any parametric model restrictions while also allowing for some sudden changes in the unknown regression function. The sudden changes in the model may correspond to discontinuity points (jumps) or higher order breaks (jumps in corresponding derivatives) as well. We firstly derive some important statistical properties for local polynomial M-smoother estimates and we will propose a statistical test to decide whether some given point of interest is significantly important for a change to occur or not. As the asymptotic distribution of the test statistic depends on quantities which are left unknownwealso introduce a bootstrap algorithm which can be used to mimic the target distribution of interest. All necessary proofs are provided together with some experimental results from a simulation study and a real data example.

• 出版日期2017