On the conditional distributions of low-dimensional projections from high-dimensional data

Abstract: We study the conditional distribution of low-dimensional projections from high-dimensional data, where the conditioning is on other low-dimensional projections. To fix ideas, consider a random d-vector Z that has a Lebesgue density and that is standardized so that $\mathbb{E}Z=0$ and $\mathbb{E}ZZ'=I_d$. Moreover, consider two projections defined by unit-vectors $\alpha$ and $\beta$, namely a response $y=\alpha'Z$ and an explanatory variable $x=\beta'Z$. It has long been known that the conditional mean of y given x is approximately linear in x$ under some regularity conditions; cf. Hall and Li [Ann. Statist. 21 (1993) 867-889]. However, a corresponding result for the conditional variance has not been available so far. We here show that the conditional variance of y given x is approximately constant in x (again, under some regularity conditions). These results hold uniformly in $\alpha$ and for most $\beta$'s, provided only that the dimension of Z is large. In that sense, we see that most linear submodels of a high-dimensional overall model are approximately correct. Our findings provide new insights in a variety of modeling scenarios. We discuss several examples, including sliced inverse regression, sliced average variance estimation, generalized linear models under potential link violation, and sparse linear modeling.