We provide a unified framework for the high-dimensional analysis of “superposition-structured” or “dirty” statistical models: where the model parameters are a “superposition” of structurally constrained parameters. We allow for any number and types of structures, and any statistical model. We consider the general class of $M$-estimators that minimize the sum of any loss function, and an instance of what we call a “hybrid” regularization, that is the infimal convolution of weighted regularization functions, one for each structural component. We provide corollaries showcasing our unified framework for varied statistical models such as linear regression, multiple regression and principal component analysis, over varied superposition structures.

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Advances in Neural Information Processing Systems (NIPS) (2013).

@article{YR13, title={Dirty Statistical Models}, author={Eunho Yang and Pradeep Ravikumar}, booktitle={Advances in Neural Information Processing Systems (NIPS)}, url="http://www.cs.utexas.edu/users/ai-lab?YR13", year={2013} }