论文标题
规范回归分位数，并应用于CEO薪酬并预测公司绩效
Canonical Regression Quantiles with application to CEO compensation and predicting company performance
论文作者
论文摘要
在使用多个回归方法进行预测时，人们经常将解释变量的线性组合视为索引。当这里有多个响应时，寻求单个索引更为复杂。一种经典的方法是使用领先规范相关性的系数。但是，基于方差的方法无法通过分位效应，缺乏鲁棒性并依赖于正常假设来分解响应。我们在这里开发了一种替代回归分数方法，并将其应用于大型公共公司和首席执行官薪酬绩效的实证研究。最初的结果非常有前途。
In using multiple regression methods for prediction, one often considers the linear combination of explanatory variables as an index. Seeking a single such index when here are multiple responses is rather more complicated. One classical approach is to use the coefficients from the leading canonical correlation. However, methods based on variances are unable to disaggregate responses by quantile effects, lack robustness, and rely on normal assumptions for inference. We develop here an alternative regression quantile approach and apply it to an empirical study of the performance of large publicly held companies and CEO compensation. The initial results are very promising.
