Rajen D. Shah develops statistical inference for high-dimensional data. His author publication list verifies work on regression, model checking and testing conditional independence.[ref]
With Jonas Peters, he introduced the generalized covariance measure statistic, based on the covariance of residuals after each response is regressed on the same conditioning variables. The resulting hypothesis test combines regression estimates with a calibrated covariance statistic. Their accompanying impossibility result explains why unrestricted distribution-free conditional independence testing needs additional structure; the regression-based method instead states approximation and moment assumptions explicitly.[ref]
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