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学者姓名：徐涛
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Abstract ：
In extreme value theory, the tail index parameter controls the tail behavior of a distribution function and is thus of primary interest in analyzing extreme events. Recent developments in modeling the tail index along with covariates have been in semi-parametric regression, but there is a lack of flexible models for time series data, especially for non stationary data. To handle such cases, this article proposes a novel tail single-index regression model incorporating locally stationary covariates to address time-varying tail behaviors. For the proposed model, we develop an estimation procedure by proposing an iterative algorithm and a selection method for the tuning parameter. The asymptotic properties of the estimators are constructed in the time-dependent context. Numerical studies and an analysis of Ozone data demonstrate the effectiveness of our model and corresponding theories.
Keyword ：
Extremes Extremes heavy-tailed distribution heavy-tailed distribution local stationarity local stationarity single-index model single-index model tail index regression tail index regression
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| GB/T 7714 | Xu, Tao , Chen, Yu , Sun, Hongfang . Tail single-index regression with locally stationary regressors [J]. | COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , 2025 , 54 (20) : 6652-6669 . |
| MLA | Xu, Tao 等. "Tail single-index regression with locally stationary regressors" . | COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 54 . 20 (2025) : 6652-6669 . |
| APA | Xu, Tao , Chen, Yu , Sun, Hongfang . Tail single-index regression with locally stationary regressors . | COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , 2025 , 54 (20) , 6652-6669 . |
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