Weighted estimation of single index models with right censored responses

In this paper, the unknown link function, the direction parameter, and the heteroscedastic variance in single index models are estimated by the random weight method under the random censorship, respectively. The central limit theory and the convergence rate of the law of the iterated logarithm for the estimator of the direction parameter are derived, respectively. The optimal convergence rates for the estimators of the link function and the heteroscedastic variance are obtained. Simulation results support the theoretical results of the paper.

个人小额信贷风险影响因素研究

随着互联网消费金融兴起,小额信贷慢慢渗入到经济生活中。然而,金融机构在获得消费信贷利润的同时,持续攀升的不良贷款率也在逼着这些信贷公司不断提高风险评估水平和控制能力。于是,个人信用风险的识别和信贷风险管理成为热门话题。本文基于一家互联网金融公司的个人信贷数据,利用未知连接函数的广义可加模型(GAMUL),分析各个特征变量如何影响个人信用风险。本文首先使用非参独立扫描(NIS)方法选择影响显著的变量,考虑了自变量与因变量之间的非线性关系,再使用GAMUL对数据建立模型。无论是变量选择还是模型分析,NIS-GAMUL都没有对自变量和响应变量进行过多的人为主观假设,完全由数据驱动,使整个模型分析具有较强的适应性和灵活性。