论文标题
尾部指数回归中不同系数模型的假设测试
Hypothesis testing for varying coefficient models in tail index regression
论文作者
论文摘要
这项研究检查了尾部指数回归中不同系数模型。不同系数模型是一个有效的半参数模型，在模型中包括大量协变量时避免了维数的诅咒。实际上，不同系数模型在均值，分位数和其他回归中很有用。尾部索引回归不是例外。但是，变化的系数模型是灵活的，但是对于应用来说，更精细，更简单的模型是优选的。因此，评估估计系数函数是否随协变量显着变化非常重要。如果模型的非线性效果较弱，则将变化的系数结构降低到更简单的模型，例如恒定或零。因此，在均值和分位回归中讨论了不同系数模型中模型评估的假设检验。但是，尾部指数回归没有结果。在这项研究中，我们研究了估计量的渐近特性，并为尾部指数回归的不同系数模型提供了假设测试方法。
This study examines the varying coefficient model in tail index regression. The varying coefficient model is an efficient semiparametric model that avoids the curse of dimensionality when including large covariates in the model. In fact, the varying coefficient model is useful in mean, quantile, and other regressions. The tail index regression is not an exception. However, the varying coefficient model is flexible, but leaner and simpler models are preferred for applications. Therefore, it is important to evaluate whether the estimated coefficient function varies significantly with covariates. If the effect of the non-linearity of the model is weak, the varying coefficient structure is reduced to a simpler model, such as a constant or zero. Accordingly, the hypothesis test for model assessment in the varying coefficient model has been discussed in mean and quantile regression. However, there are no results in tail index regression. In this study, we investigate the asymptotic properties of an estimator and provide a hypothesis testing method for varying coefficient models for tail index regression.
