A Bayesian Approach to Robust Binary Nonparametric Regression
提出一种贝叶斯方法用于二元非参数回归，假设链接函数的自变量是解释变量及其乘法交互的加性函数，使用平滑样条估计并集成平滑参数，对异常值稳健，适用于多种链接函数，并通过模拟验证其优于两种现有估计量。
Abstract This article presents a Bayesian approach to binary nonparametric regression that assumes that the argument of the link is an additive function of the explanatory variables and their multiplicative interactions. The article makes the following contributions. First, a comprehensive approach is presented in which the function estimates are smoothing splines with the smoothing parameters integrated out and the estimates are made robust to outliers. Second, the approach can handle a wide range of link functions. Third, efficient state-space-based algorithms are used to carry out the computations. Fourth, an extensive set of simulations is carried out, which show that the Bayesian estimator works well and compares favorably to two estimators that have recently been proposed and used in practice.