Grand Lebesgue Spaces norm estimates for multivariate functional operations

2018/05/05 by E. Ostrovsky, Ostrovsky, E., L. Sirota +1
Computer Science · Mathematics · #Advanced Harmonic Analysis Research #Advanced Mathematical Modeling in Engineering #FOS: Mathematics #Functional Analysis (math.FA) #Mathematical Approximation and Integration

paper · pdf · doi:10.48550/arxiv.1805.01982

openalex publication_date 2018/05/05 · openalex created_date 2018/05/17 · openalex updated_date 2026/07/28

Abstract

We intend to derive the moment and exponential tail estimates for the so-called bivariate or more generally multivariate functional operations, not necessary to be linear or even multilinear. We will show also the strong or at last weak (i.e. up to multiplicative constant) exactness of obtained estimates.

Citations

• Boundedness of Operators in Bilateral Grand Bebesgue Spaces with Exact\n and Weakly Exact Constant Calculation
• Relations between exponential tails, moments and moment generating functions for random variables and vectors
• Bilinear Spherical Maximal Function
• Weighted estimates for the multilinear maximal function on the upper half-spaces
• Improved bound for the bilinear Bochner-Riesz operator

Related

• Maximal and other operators in exponential Orlicz and Grand Lebesgue Spaces
• Relations between exponential tails, moments and moment generating functions for random variables and vectors
• Generalized Grand Lebesgue Spaces norm estimations for some operators
• Sharp Grand Lebesgue Spaces norm estimation for infimal convolution
• Equivalence between tails, Grand Lebesgue Spaces and Orlicz norms for random variables without Cramer's condition