Double-Estimation-Friendly Inference for High-Dimensional Misspecified Models
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All models may be wrong---but that is not necessarily a problem for inference. Consider the standard
In this expository paper we explore this phenomenon, and propose methodology for high-dimensional regression settings that respects the DEF property. We advocate specifying (sparse) generalised linear regression models for both
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2168-8745
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Engineering and Physical Sciences Research Council (EP/R013381/1)