Semiparametric Tail Index Regression
提出一种新的半参数尾部指数回归模型，用于解释极端事件如何受解释变量影响，并给出参数和非参数成分的一致估计及渐近正态性，通过模拟和实际数据验证其有效性。
–Understanding why extreme events occur is often of major scientific interest in many fields. The occurrence of these events naturally depends on explanatory variables, but there is a severe lack of flexible models with asymptotic theory for understanding this dependence, especially when variables can affect the outcome nonlinearly. This article proposes a novel semiparametric tail index regression model to fill the gap for this purpose. We construct consistent estimators for both parametric and nonparametric components of the model, establish the corresponding asymptotic normality properties for these components that can be applied for further inference, and illustrate the usefulness of the model via extensive Monte Carlo simulation and the analysis of return on equity data and Alps meteorology data.