Error bounds for high–dimensional Edgeworth expansions for some tests on covariance matrices

نویسنده

  • Hirofumi Wakaki
چکیده

Problems of testing three hypotheses : (i) equality of covariance matrices of several multivariate normal populations, (ii) sphericity, and (iii) that a covariance matrix is equal to a specified one, are treated. High–dimensional Edgeworth expansions of the null distributions of the modified likelihood ratio test statistics are derived. Computable error bounds of the expansions are derived for each expansions. The Edgeworth expansion and its error bound for non–null distribution of the test statistic for (iii) are also derived.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

High Dimensional Sparse Covariance Estimation: Accurate Thresholds for the Maximal Diagonal Entry and for the Largest Correlation Coefficient

The maxima of many independent, or weakly dependent, random variables, and their corresponding thresholds for given right tail probabilities are classical and well studied problems. In this paper we focus on two specific cases of interest related to estimation and hypothesis testing of high dimensional sparse covariance matrices. These are the distribution of the maximal diagonal entry of a sam...

متن کامل

Non-Asymptotic Results for Cornish-Fisher Ex- pansions

We get the computable error bounds for generalized Cornish-Fisher expansions for quantiles of statistics provided that the computable error bounds for Edgeworth-Chebyshev type expansions for distributions of these statistics are known. The results are illustrated by examples.

متن کامل

Estimation of High-dimensional Prior and Posterior Covariance Matrices in Kalman Filter Variants

This work studies the effect of using Monte Carlo based methods to estimate high-dimensional systems. Recent focus in the geosciences has been on representing the atmospheric state using a probability density function, and, for extremely high-dimensional systems, various sample based Kalman filter techniques have been developed to address the problem of real-time assimilation of system informat...

متن کامل

Asymptotic expansions in mean and covariance structure analysis

1. Abstract Asymptotic expansions of the distributions of parameter estimators in mean and covariance structures are derived. The parameters may be common to or specific in means and covariances of observable variables. The means are possibly structured by the common/specific parameters. First, the distributions of the parameter estimators standardized by the population asymptotic standard erro...

متن کامل

Two Sample Tests for High - Dimensional Covariance Matrices

We propose two tests for the equality of covariance matrices between two high-dimensional populations. One test is on the whole variance–covariance matrices, and the other is on off-diagonal sub-matrices, which define the covariance between two nonoverlapping segments of the high-dimensional random vectors. The tests are applicable (i) when the data dimension is much larger than the sample size...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2007