Moment and tail estimates and Banach space valued Non-Central Limit Theorem (NCLT) for sums of multi-indexed random variables, processes and fields

Abstract

We derive in this preprint the moment and exponential tail estimates, sufficient conditions for the Non-Central Limit Theorem (NCLT) in the ordinary one-dimensional space as well as in the space of continuous functions for the properly (natural) normalized multi-indexed sums of function of random variables, processes or fields (r.f.), on the other words V-statistics, parametric, in general case. We construct also some examples in order to show the exactness of obtained estimates. We will use the theory of the so-called degenerate approximation of the functions of several variables as well as the theory of Grand Lebesgue Spaces (GLS) of measurable functions (random variables).

References in corpus (5)

• Schlomilch and Bell Series for Bessel's Functions, with Probabilistic Applications
• Relations between exponential tails, moments and moment generating functions for random variables and vectors
• Sharp moment estimates for polynomial martingales
• Theory of approximation and continuity of random processes
• Uniform Limit Theorem and tail estimates for parametric u-statistics