Reduction of variance for Gaussian densities via restriction to convex sets

Let X be a random vector with values in n and a Gaussian density f. Let Y be a random vector whose density can be factored as k · f, where k is a logarithmically concave function on n. We prove that the covariance matrix of X dominates the covariance matrix of Y by a positive semidefinite matrix. When k is the indicator function of a compact convex set A of positive measure the difference is positive definite. If A and X are both symmetric Var(a · X) is bounded above by an expression which is always strictly less than Var(a · X) for every a [set membership, variant] n. Finally some counterexamples are given to show that these results cannot be extended to the general case where f is any logarithmically concave density.