Non-integrable Stable Approximation by Stein’s Method
Author(s) -
Peng Chen,
Ivan Nourdin,
Lihu Xu,
Xiaochuan Yang,
Rui Zhang
Publication year - 2021
Publication title -
journal of theoretical probability
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.671
H-Index - 42
eISSN - 1572-9230
pISSN - 0894-9840
DOI - 10.1007/s10959-021-01094-5
Subject(s) - mathematics , stein's method , integrable system , moment (physics) , limit (mathematics) , sequence (biology) , approximation error , alpha (finance) , mathematical analysis , pure mathematics , statistics , intrinsic metric , construct validity , physics , genetics , classical mechanics , biology , convex metric space , fixed point , psychometrics
We develop Stein's method for $\alpha$-stable approximation with $\alpha\in(0,1]$, continuing the recent line of research by Xu \cite{lihu} and Chen, Nourdin and Xu \cite{C-N-X} in the case $\alpha\in(1,2).$ The main results include an intrinsic upper bound for the error of the approximation in a variant of Wasserstein distance that involves the characterizing differential operators for stable distributions, and an application to the generalized central limit theorem. Due to the lack of first moment for the approximating sequence in the latter result, we appeal to an additional truncation procedure and investigate fine regularity properties of the solution to Stein's equation.
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