Abstract
To provide an introduction to classical modern techniques of the theory of stochastic canonical equations, we examine the first stochastic canonical equation, which was found in [Gir12] twenty five years ago. In this chapter we will consider symmetric matrices Ξ n with random entries. In the general case, the entries of the random matrix \( {\Xi _n} = (\xi _{ij}^{(n)}) \) have an arbitrary form and their expectations may not be equal to zero. If the entries \( \xi _{ij}^{(n)},\;i \ge j,\;\;i,j = 1,\; \ldots ,\;n \) of the symmetric random matrix Ξ n are independent, then it follows then from the main theorem of the theory of random matrices that the normalized spectral function µ n (x) of such a matrix can be approximately replaced by the nonrandom spectral function Eµ n (x) (see [Gir12, pp. 217–219]). The main problem is to find all limit of spectral function Eµ n (x). Without any additional assumptions for random variables \( \xi _{ij}^{(n)} \) this problem seems trivial, since any distribution function can be a limiting function. We shall make the following assumptions, which are not too restrictive and are confirmed by numerous problems: the random variables \( \xi _{ij}^{(n)} \) are equal to asymptotically constant entries \( a_{ij}^{(n)} + \nu _{ij}^{(n)},\;i, j = 1, \ldots , n, where a_{ij}^{(n)} \) are nonrandom variables, \( \nu _{ij}^{(n)},\;i, j = 1, \ldots , n \) are infinitesimal and the vector rows of the random matrices (a ij + v ij ) are asymptotically constant. It was proved in [Gir12, p.241] that the normalized spectral function of the ACE-matrix (a ij + v ij ) for large n approaches the normalized spectral function of the matrix (a ij + γ ij ) where γ ij , i > j, are infinitely divisible independent random variables with the characteristic functions exp {E exp(isv ij ) - 1}. In this chapter, we show that it is possible to get a canonical equation for the Stieltjes transformations of the limit spectral functions of the matrix (a ij + γ ij ).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.