Tridiagonal Random Matrix: Gaussian Fluctuations and Deviations
Deng Zhang ()
Additional contact information
Deng Zhang: Shanghai Jiao Tong University
Journal of Theoretical Probability, 2017, vol. 30, issue 3, 1076-1103
Abstract:
Abstract This paper is devoted to the Gaussian fluctuations and deviations of the traces of tridiagonal random matrices. Under quite general assumptions, we prove that the traces are approximately normally distributed. A Multi-dimensional central limit theorem is also obtained here. These results have several applications to various physical models and random matrix models, such as the Anderson model, the random birth–death Markov kernel, the random birth–death Q matrix and the $$\beta $$ β -Hermite ensemble. Furthermore, under an independent-and-identically-distributed condition, we also prove the large deviation principle as well as the moderate deviation principle for the traces.
Keywords: Central limit theorem; Moderate deviation; Large deviation; Tridiagonal random matrix; 60B20; 60F05; 60F10 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://link.springer.com/10.1007/s10959-016-0683-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:jotpro:v:30:y:2017:i:3:d:10.1007_s10959-016-0683-7
Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10959
DOI: 10.1007/s10959-016-0683-7
Access Statistics for this article
Journal of Theoretical Probability is currently edited by Andrea Monica
More articles in Journal of Theoretical Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().