منابع مشابه
Testing proportionality of two large-dimensional covariance matrices
Testing the proportionality of two large-dimensional covariance matrices is studied. Based on modern random matrix theory, a pseudo-likelihood ratio statistic is proposed and its asymptotic normality is proved as the dimension and sample sizes tend to infinity proportionally.
متن کاملA new test for the proportionality of two large-dimensional covariance matrices
Let x1, . . . ,xn1+1 iid ∼ Np(μ1,Σ1) and y1, . . . ,yn2+1 iid ∼ Np(μ2,Σ2) be two independent random samples, where n1 6 p < n2. In this article, we propose a new test for the proportionality of two large p × p covariance matrices Σ1 and Σ2. By applying modern random matrix theory, we establish the asymptotic normality property for the proposed test statistic as (p, n1, n2) → ∞ together with the...
متن کاملAn Axiomatic Approach to Proportionality Between Matrices
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive o...
متن کاملInformation Covariance Matrices for Multivariate Burr III and Logistic Distributions
Main result of this paper is to derive the exact analytical expressions of information and covariance matrices for multivariate Burr III and logistic distributions. These distributions arise as tractable parametric models in price and income distributions, reliability, economics, Human population, some biological organisms to model agricultural population data and survival data. We showed that ...
متن کاملLarge Dynamic Covariance Matrices
Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH family started by Engle (1982). In ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1987
ISSN: 0090-5364
DOI: 10.1214/aos/1176350372