Convergence in distribution norms in the CLT for non identically distributed random variables

We study the convergence in distribution norms in the Central Limit Theorem for non identical distributed random variables that is where Zi, i ∈ N, are centred independent random variables and G is a Gaussian random variable. We also consider local developments (Edgeworth expansion). This kind of results is well understood in the case of smooth test functions f. If one deals with measurable and bounded test functions (convergence in total variation distance), a well known theorem due to Prohorov shows that some regularity condition for the law of the random variables Zi, i ∈ N, on hand is needed. Essentially, one needs that the law of Zi is locally lower bounded by the Lebesgue measure (Doeblin’s condition). This topic is also widely discussed in the literature. Our main contribution is to discuss convergence in distribution norms, that is to replace the test function f by some derivative ∂αf and to obtain upper bounds for εn(∂αf) in terms of the infinite norm of f.