26,502 research outputs found
Central limit theorems for functionals of large sample covariance matrix and mean vector in matrix-variate location mixture of normal distributions
In this paper we consider the asymptotic distributions of functionals of the
sample covariance matrix and the sample mean vector obtained under the
assumption that the matrix of observations has a matrix-variate location
mixture of normal distributions. The central limit theorem is derived for the
product of the sample covariance matrix and the sample mean vector. Moreover,
we consider the product of the inverse sample covariance matrix and the mean
vector for which the central limit theorem is established as well. All results
are obtained under the large-dimensional asymptotic regime where the dimension
and the sample size approach to infinity such that when the sample covariance matrix does not need to be invertible and
otherwise.Comment: 30 pages, 8 figures, 1st revisio
Deciding the dimension of effective dimension reduction space for functional and high-dimensional data
In this paper, we consider regression models with a Hilbert-space-valued
predictor and a scalar response, where the response depends on the predictor
only through a finite number of projections. The linear subspace spanned by
these projections is called the effective dimension reduction (EDR) space. To
determine the dimensionality of the EDR space, we focus on the leading
principal component scores of the predictor, and propose two sequential
testing procedures under the assumption that the predictor has an
elliptically contoured distribution. We further extend these procedures and
introduce a test that simultaneously takes into account a large number of
principal component scores. The proposed procedures are supported by theory,
validated by simulation studies, and illustrated by a real-data example. Our
methods and theory are applicable to functional data and high-dimensional
multivariate data.Comment: Published in at http://dx.doi.org/10.1214/10-AOS816 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Algebraic statistical models
Many statistical models are algebraic in that they are defined in terms of
polynomial constraints, or in terms of polynomial or rational parametrizations.
The parameter spaces of such models are typically semi-algebraic subsets of the
parameter space of a reference model with nice properties, such as for example
a regular exponential family. This observation leads to the definition of an
`algebraic exponential family'. This new definition provides a unified
framework for the study of statistical models with algebraic structure. In this
paper we review the ingredients to this definition and illustrate in examples
how computational algebraic geometry can be used to solve problems arising in
statistical inference in algebraic models
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