SMOOTHED QUANTILE REGRESSION PROCESSES FOR BINARY RESPONSE MODELS
研究了带有线性分位数约束的二元响应模型，通过无限分位数估计量推导出均匀线性化表示，发现与连续响应情形的差异，并展示了该方法无需限制连接函数形式或处理异方差性即可有效估计二元选择概率。
In this article, we consider binary response models with linear quantile restrictions. Considerably generalizing previous research on this topic, our analysis focuses on an infinite collection of quantile estimators. We derive a uniform linearization for the properly standardized empirical quantile process and discover some surprising differences with the setting of continuously observed responses. Moreover, we show that considering quantile processes provides an effective way of estimating binary choice probabilities without restrictive assumptions on the form of the link function, heteroskedasticity, or the need for high dimensional nonparametric smoothing necessary for approaches available so far. A uniform linear representation and results on asymptotic normality are provided, and the connection to rearrangements is discussed.