A Permutation Test for High-Dimensional Covariance Matrix
نویسندگان
چکیده
منابع مشابه
A new test for sphericity of the covariance matrix for high dimensional data
AMS subject classifications: 62H10 62H15 Keywords: Covariance matrix Hypothesis testing High-dimensional data analysis a b s t r a c t In this paper we propose a new test procedure for sphericity of the covariance matrix when the dimensionality, p, exceeds that of the sample size, N = n + 1. Under the assumptions that (A) 0 < trΣ the concentration, a new statistic is developed utilizing the rat...
متن کاملA New Test on High-Dimensional Mean Vector Without Any Assumption on Population Covariance Matrix
Abstract We propose a new test for the equality of the mean vectors between a two groups with the same number of the observations in highdimensional data. The existing tests for this problem require a strong condition on the population covariance matrix. The proposed test in this paper does not require such conditions for it. This test will be obtained in a general model, that is, the data need...
متن کاملHigh dimensional covariance matrix estimation using a factor model
High dimensionality comparable to sample size is common in many statistical problems. We examine covariance matrix estimation in the asymptotic framework that the dimensionality p tends to∞ as the sample size n increases. Motivated by the Arbitrage Pricing Theory in finance, a multi-factor model is employed to reduce dimensionality and to estimate the covariance matrix. The factors are observab...
متن کاملHigh Dimensional Inverse Covariance Matrix Estimation via Linear Programming
This paper considers the problem of estimating a high dimensional inverse covariance matrix that can be well approximated by “sparse” matrices. Taking advantage of the connection between multivariate linear regression and entries of the inverse covariance matrix, we propose an estimating procedure that can effectively exploit such “sparsity”. The proposed method can be computed using linear pro...
متن کاملRandom matrix theory and estimation of high-dimensional covariance matrices
This projects aims to present significant results of random matrix theory in regards to the principal component analysis, including Wigner’s semicircular law and Marčenko-Pastur law describing limiting distribution of large dimensional random matrices. The work bases on the large dimensional data assumptions, where both the number of variables and sample size tends to infinity, while their rati...
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: Journal of Physics: Conference Series
سال: 2021
ISSN: 1742-6588,1742-6596
DOI: 10.1088/1742-6596/1865/4/042027