195 research outputs found
Separation of the largest eigenvalues in eigenanalysis of genotype data from discrete subpopulations
We present a mathematical model, and the corresponding mathematical analysis,
that justifies and quantifies the use of principal component analysis of
biallelic genetic marker data for a set of individuals to detect the number of
subpopulations represented in the data. We indicate that the power of the
technique relies more on the number of individuals genotyped than on the number
of markers.Comment: Corrected typos in Section 3.1 (M=120, N=2500) and proof of Lemma
Semigroups of distributions with linear Jacobi parameters
We show that a convolution semigroup of measures has Jacobi parameters
polynomial in the convolution parameter if and only if the measures come
from the Meixner class. Moreover, we prove the parallel result, in a more
explicit way, for the free convolution and the free Meixner class. We then
construct the class of measures satisfying the same property for the two-state
free convolution. This class of two-state free convolution semigroups has not
been considered explicitly before. We show that it also has Meixner-type
properties. Specifically, it contains the analogs of the normal, Poisson, and
binomial distributions, has a Laha-Lukacs-type characterization, and is related
to the case of quadratic harnesses.Comment: v3: the article is merged back together with arXiv:1003.4025. A
significant revision following suggestions by the referee. 2 pdf figure
One-sided Cauchy-Stieltjes Kernel Families
This paper continues the study of a kernel family which uses the
Cauchy-Stieltjes kernel in place of the celebrated exponential kernel of the
exponential families theory. We extend the theory to cover generating measures
with support that is unbounded on one side. We illustrate the need for such an
extension by showing that cubic pseudo-variance functions correspond to
free-infinitely divisible laws without the first moment. We also determine the
domain of means, advancing the understanding of Cauchy-Stieltjes kernel
families also for compactly supported generating measures
Spectral measure of heavy tailed band and covariance random matrices
We study the asymptotic behavior of the appropriately scaled and possibly
perturbed spectral measure of large random real symmetric matrices with
heavy tailed entries. Specifically, consider the N by N symmetric matrix
whose (i,j) entry is where is an infinite array of i.i.d real variables with common
distribution in the domain of attraction of an -stable law,
, and is a deterministic function. For a random diagonal
independent of and with appropriate rescaling , we
prove that the distribution of converges in
mean towards a limiting probability measure which we characterize. As a special
case, we derive and analyze the almost sure limiting spectral density for
empirical covariance matrices with heavy tailed entries.Comment: 31 pages, minor modifications, mainly in the regularity argument for
Theorem 1.3. To appear in Communications in Mathematical Physic
Genus expansion for real Wishart matrices
We present an exact formula for moments and cumulants of several real
compound Wishart matrices in terms of an Euler characteristic expansion,
similar to the genus expansion for complex random matrices. We consider their
asymptotic values in the large matrix limit: as in a genus expansion, the terms
which survive in the large matrix limit are those with the greatest Euler
characteristic, that is, either spheres or collections of spheres. This
topological construction motivates an algebraic expression for the moments and
cumulants in terms of the symmetric group. We examine the combinatorial
properties distinguishing the leading order terms. By considering higher
cumulants, we give a central limit-type theorem for the asymptotic distribution
around the expected value
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