Composite quasi-likelihood for single-index models with massive datasets

Rong Jiang, Meng-Fan Guo & Xin Liu
The single-index models (SIMs) provide an efficient way of coping with high-dimensional nonparametric estimation problems and avoid the “curse of dimensionality.” Many existing estimation procedures for SIMs were built on least square loss, which is popular for its mathematical beauty but is non-robust to non-normal errors and outliers. This article addressed the question of both robustness and efficiency of estimation methods based on a new data-driven weighted linear combination of convex loss functions instead of...
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