Multi-Index Binary Response Analysis of Large Data Sets
提出一种多指标二元响应模型，将大量回归变量组合成少数因子，再用参数或非参数方法估计连接函数，无需事先知道响应与变量的关系，并给出因子载荷显著性检验的新渐近结果。实证中将124个变量降为4个因子。
Abstract We propose a multi-index binary response model for analyzing large databases (i.e., with many regressors). We combine many regressors into factors (or indexes) and then estimate the link function via parametric or nonparametric methods. Neither the estimation of factors nor the determination of the number of factors requires ex ante knowledge of the link between the response and regressors. Furthermore, applying perturbation theory, we furnish a new asymptotic result to facilitate significance tests of factor loadings. We illustrate this approach with an empirical application in which we reduced dimensionality from 124 regressors to 4 factors. Keywords: : Customer relationship marketingDiscrete choiceFactor modelInverse regressionSemiparametric estimationSliced average variance estimation