Showing papers by "T. W. Anderson published in 1989"
TL;DR: The asymptotic distribution of the maximum likelihood estimator derived under normality is shown to be valid generally if the different latent variables are independent (not just uncorrelated) and tests of the covariance structure are also asymPTotically robust.
82 citations
Book•
01 Sep 1989
31 citations
TL;DR: In the balanced multivariate components of variance the likelihood ratio criterion depends on the roots of a determinantal equation involving the "between" and "within" matric sums of squares as discussed by the authors.
15 citations
01 Jan 1989
TL;DR: In this paper, the asymptotic distribution of the characteristic roots and vectors of one Wishart matrix in the metric of another as the two degrees of freedom increase in fixed proportion is obtained.
Abstract: The asymptotic distribution of the characteristic roots and vectors of one Wishart matrix in the metric of another as the two degrees of freedom increase in fixed proportion is obtained. In the balanced one-way multivariate analysis of variance these two matrices are the sample effect and error covariance matrices, and the numbers of degrees of freedom are (approximately) proportional to the number of classes. The maximum likelihood estimate of the effect covariance matrix of a given rank depends on the characteristic roots and vectors.
13 citations
01 Jun 1989
TL;DR: In this article, Lindeberg-type conditions are used to establish asymptotic normality of sample regression and autoregression coefficients, and they are applied to establish the robustness of a statistical procedure under conditions more general than those under which the procedure is derived.
Abstract: : A statistical procedure is asymptotically robust if its large-sample properties hold under conditions more general than the conditions under which the procedure is derived. The justification of such procedures is often based directly or indirectly on a central limit theorem. In this paper Lindeberg-type conditions are utilized to establish asymptotic normality of sample regression and autoregression coefficients.
5 citations
4 citations
3 citations