EN
Negative association for a family of random variables $(X_i)$ means that for any coordinatewise increasing functions f,g we have
$𝔼 (X_{i₁},...,X_{i_k}) g(X_{j₁},...,X_{j_l}) ≤ 𝔼 f(X_{i₁},...,X_{i_k}) 𝔼 g(X_{j₁},...,X_{j_l})$
for any disjoint sets of indices (iₘ), (jₙ). It is a way to indicate the negative correlation in a family of random variables. It was first introduced in 1980s in statistics by Alem & Saxena and Joag-Dev & Proschan, and brought to convex geometry in 2005 by Wojtaszczyk & Pilipczuk to prove the Central Limit Theorem for Orlicz balls. The paper gives a relatively simple proof of negative association of absolute values for a wide class of measures tied to generalized Orlicz balls, including the uniform measures on such balls.