Nonlinear Boosting with Multiple Testing in High-Dimensional Generalised Linear Models with Binary Responses
高维二元响应广义线性模型中的多重检验非线性提升
This paper proposes a nonlinear boosting with multiple testing (BMT) approach to variable selection in high-dimensional generalised linear models with binary responses. At each stage of the BMT procedure, the model is updated by adding only the most significant covariate, conditional on those already selected in previous stages, while taking into account the multiple testing nature of the problem. It is shown that, under the stated conditions, the BMT procedure selects all…