One of the most widely used properties of the multivariate Gaussian distribution, besides its tail behavior, is the fact that conditional means are linear and that conditional variances are constant. We here show that this property is also shared, in an approximate sense, by a large class of non-Gaussian distributions. We allow for several conditioning variables and we provide explicit non-asymptotic results, whereby we extend earlier findings of Hall and Li (Ann. Statist. 21 (1993) 867–889) and Leeb (Ann. Statist. 41 (2013) 464–483).
"On conditional moments of high-dimensional random vectors given lower-dimensional projections." Bernoulli 24 (1) 565 - 591, February 2018. https://doi.org/10.3150/16-BEJ888