Statistical version of the central limit theorem for vector-valued random fields

The classical central limit theorem due to Newman for real-valued strictly stationary associated random fields is generalized to strictly stationary quasi-associated vector-valued random fields comprising, in particular, positively or negatively associated fields with finite second moments. We also establish a version of the CLT with random matrix normalization which allows us to construct approximate confidence intervals for the unknown mean vector.