Probability densities and distributions for spiked and general variance Wishart $\beta$-ensembles
Peter J. Forrester

TL;DR
This paper introduces an alternative recursive construction for spiked and general variance Wishart beta-ensembles, deriving explicit joint eigenvalue PDFs and analyzing the distribution of the largest eigenvalue, extending prior results.
Contribution
It provides a new recursive method to construct Wishart beta-ensembles and derives explicit eigenvalue distributions, simplifying and extending previous findings.
Findings
Explicit joint eigenvalue PDFs for spiked and general variance Wishart beta-ensembles.
Distribution of the largest eigenvalue in quaternion Wishart matrices linked to last passage percolation.
Simplified derivation of eigenvalue PDFs compared to earlier methods.
Abstract
A Wishart matrix is said to be spiked when the underlying covariance matrix has a single eigenvalue different from unity. As increases through , a gap forms from the largest eigenvalue to the rest of the spectrum, and with of order the scaled largest eigenvalues form a well defined parameter dependent state. Recent works by Bloemendal and Vir\'ag [BV], and Mo, have quantified this parameter dependent state for real Wishart matrices from different viewpoints, and the former authors have done similarly for the spiked Wishart -ensemble. The latter is defined in terms of certain random bidiagonal matrices. We use a recursive structure to give an alternative construction of the spiked and more generally the general variance Wishart -ensemble, and we give the exact form of the joint eigenvalue PDF for the two matrices in the recurrence. In the case…
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