32,130 research outputs found
Robust Modeling Using Non-Elliptically Contoured Multivariate t Distributions
Models based on multivariate t distributions are widely applied to analyze
data with heavy tails. However, all the marginal distributions of the
multivariate t distributions are restricted to have the same degrees of
freedom, making these models unable to describe different marginal
heavy-tailedness. We generalize the traditional multivariate t distributions to
non-elliptically contoured multivariate t distributions, allowing for different
marginal degrees of freedom. We apply the non-elliptically contoured
multivariate t distributions to three widely-used models: the Heckman selection
model with different degrees of freedom for selection and outcome equations,
the multivariate Robit model with different degrees of freedom for marginal
responses, and the linear mixed-effects model with different degrees of freedom
for random effects and within-subject errors. Based on the Normal mixture
representation of our t distribution, we propose efficient Bayesian inferential
procedures for the model parameters based on data augmentation and parameter
expansion. We show via simulation studies and real examples that the
conclusions are sensitive to the existence of different marginal
heavy-tailedness
Spectral distributions of adjacency and Laplacian matrices of random graphs
In this paper, we investigate the spectral properties of the adjacency and
the Laplacian matrices of random graphs. We prove that: (i) the law of large
numbers for the spectral norms and the largest eigenvalues of the adjacency and
the Laplacian matrices; (ii) under some further independent conditions, the
normalized largest eigenvalues of the Laplacian matrices are dense in a compact
interval almost surely; (iii) the empirical distributions of the eigenvalues of
the Laplacian matrices converge weakly to the free convolution of the standard
Gaussian distribution and the Wigner's semi-circular law; (iv) the empirical
distributions of the eigenvalues of the adjacency matrices converge weakly to
the Wigner's semi-circular law.Comment: Published in at http://dx.doi.org/10.1214/10-AAP677 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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