-
作者：Croux, C; Haesbroeck, G
作者单位：Universite Libre de Bruxelles; University of Liege
摘要：A robust principal component analysis can be easily performed by computing the eigenvalues and eigenvectors of a robust estimator of the covariance or correlation matrix. In this paper we derive the influence functions and the corresponding asymptotic variances for these robust estimators of eigenvalues and eigenvectors. The behaviour of several of these estimators is investigated by a simulation study. It turns out that the theoretical results and simulations favour the use of S-estimators, s...
-
作者：Davis, RA; Dunsmuir, WTM; Wang, Y
作者单位：Colorado State University System; Colorado State University Fort Collins; University of New South Wales Sydney
摘要：This paper develops a practical approach to diagnosing the existence of a latent stochastic process in the mean of a Poisson regression model. The asymptotic distribution of standard generalised linear model estimators is derived for the case where an autocorrelated latent process is present. Simple formulae for the effect of autocovariance on standard errors of the regression coefficients are also provided. Methods for adjusting for the severe bias in previously proposed estimators of autocov...
-
作者：Martin, RJ
作者单位：University of Sheffield
摘要：For regularly-spaced observations with the Gaussian autocorrelation function, the finite and infinite autoregressive and moving-average representations can be obtained theoretically. This allows exact Gaussian maximum likelihood to be performed very accurately, and gives a simple exact simulation method.
-
作者：Kent, JT; Dryden, IL; Anderson, CR
作者单位：University of Leeds; University of Nottingham
摘要：Grenander & Miller (1994) describe a model for representing amorphous two-dimensional objects with no obvious landmark. Each object is represented by n vertices around its perimeter, and is described by deforming an n-sided regular polygon using edge transformations. A multivariate normal distribution with a block circulant covariance matrix is used to model these edge transformations. The purpose of this paper is to describe in detail the statistical properties of this multivariate model and ...
