Tensor Methods for Additive Index Models under Discordance and Heterogeneity

Understand

Motivated by the sampling problems and heterogeneity issues common in high- dimensional big datasets, we consider a class of discordant additive index models.

• We propose method of moments based procedures for estimating the indices of such discordant additive index models in both low and high-dimensional settings.
• Our estimators are based on factorizing certain moment tensors and are also applicable in the overcomplete setting, where the number of indices is more than the dimensionality of the datasets.
• Furthermore, we provide rates of convergence of our estimator in both high and low-dimensional setting.