zbMATH — the first resource for mathematics

Examples
Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

Operators
a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
Fields
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Asymptotics of sample eigenstructure for a large dimensional spiked covariance model. (English) Zbl 1134.62029
Summary: This paper deals with a multivariate Gaussian observation model where the eigenvalues of the covariance matrix are all one, except for a finite number which are larger. Of interest is the asymptotic behavior of the eigenvalues of the sample covariance matrix when the sample size and the dimension of the observations both grow to infinity so that their ratio converges to a positive constant. When a population eigenvalue is above a certain threshold and of multiplicity one, the corresponding sample eigenvalue has a Gaussian limiting distribution. There is a “phase transition” of the sample eigenvectors in the same setting. Another contribution here is a study of the second order asymptotics of sample eigenvectors when corresponding eigenvalues are simple and sufficiently large.
MSC:
62H10Multivariate distributions of statistics
62E20Asymptotic distribution theory in statistics
15A18Eigenvalues, singular values, and eigenvectors
15A52Random matrices (MSC2000)
Software:
fda (R)