Mathematical Problems in Engineering

Volume 2016, Article ID 8975902, 25 pages

http://dx.doi.org/10.1155/2016/8975902

## Near-Exact Distributions for Likelihood Ratio Statistics Used in the Simultaneous Test of Conditions on Mean Vectors and Patterns of Covariance Matrices

^{1}Centro de Matemática e Aplicações (CMA), FCT/UNL, 2829-516 Caparica, Portugal^{2}Departamento de Matemática, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal^{3}Departamento de Economia e Gestão, Instituto Politécnico de Setúbal, 2910-761 Setúbal, Portugal

Received 31 May 2015; Accepted 23 November 2015

Academic Editor: Andrzej Swierniak

Copyright © 2016 Carlos A. Coelho et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The authors address likelihood ratio statistics used to test simultaneously conditions on mean vectors and patterns on covariance matrices. Tests for conditions on mean vectors, assuming or not a given structure for the covariance matrix, are quite common, since they may be easily implemented. But, on the other hand, the practical use of simultaneous tests for conditions on the mean vectors and a given pattern for the covariance matrix is usually hindered by the nonmanageability of the expressions for their exact distribution functions. The authors show the importance of being able to adequately factorize the c.f. of the logarithm of likelihood ratio statistics in order to obtain sharp and highly manageable near-exact distributions, or even the exact distribution in a highly manageable form. The tests considered are the simultaneous tests of equality or nullity of means and circularity, compound symmetry, or sphericity of the covariance matrix. Numerical studies show the high accuracy of the near-exact distributions and their adequacy for cases with very small samples and/or large number of variables. The exact and near-exact quantiles computed show how the common chi-square asymptotic approximation is highly inadequate for situations with small samples or large number of variables.

#### 1. Introduction

Testing conditions on mean vectors is a common procedure in multivariate statistics. Often a given structure is assumed for the covariance matrix, without testing it, or otherwise this test to the covariance structure is carried out apart. This is often due to the fact that the exact distribution of the test statistics used to test simultaneously conditions on mean vectors and patterns on covariance matrices is too elaborate to be used in practice. The authors show how this problem may be overcome with the development of very sharp and manageable near-exact distributions for the test statistics. These distributions may be obtained from adequate factorizations of the characteristic function (c.f.) of the logarithm of the likelihood ratio (l.r.) statistics used for these tests.

The conditions tested on mean vectors are(i)the equality of all the means in the mean vector,(ii)the nullity of all the means in the mean vectorand the patterns tested on covariance matrices are(i)circularity,(ii)compound symmetry,(iii)sphericity.

Let be a random vector with . The covariance matrix is said to be circular, or circulant, if , , with where , for ; .

For example, for and , we have

Besides the almost obvious area of times series analysis, there is a wealth of other areas and research fields where circular or circulant matrices arise, such as statistical signal processing, information theory and cryptography, biological sciences, psychometry, quality control, and signal detection, as well as spatial statistics and engineering, when observations are made on the vertices of a regular polygon.

We say that a positive-definite covariance matrix is compound-symmetric if we can write For example, for , we have

If, in (3), , we say that the matrix is spheric.

The l.r. tests for equality and nullity of means, assuming circularity, and the l.r. tests for the simultaneous test of equality or nullity of means and circularity of the covariance matrix were developed by [1], while the test for equality of means, assuming compound symmetry, and the test for equality of means and compound symmetry were formulated by [2] and the test for nullity of the means, assuming compound symmetry, and the simultaneous test for nullity of the means and compound symmetry of the covariance matrix were worked out by [3]. The exact distribution for the l.r. test statistic for the simultaneous test of equality of means and circularity of the covariance matrix was obtained in [4] and is briefly referred to in Section 2, for the sake of completeness, while near-exact distributions for the l.r. test statistic for the simultaneous test of nullity of the means and circularity of the covariance matrix are developed in Section 3. Near-exact distributions for the l.r. test statistics for the simultaneous test of equality and nullity of the means and compound symmetry of the covariance matrix are developed in Sections 4 and 5, using a different approach from the one used in Section 3. The l.r. statistics for the tests of equality and nullity of all means, assuming sphericity of the covariance matrix, may be analyzed in Appendix C and the l.r. statistics for the simultaneous tests of equality and nullity of all means and sphericity, together with the development of near-exact distributions for these statistics, may be examined in Sections 6 and 7.

Since, as referred above, the exact distributions for the statistics for the simultaneous tests of conditions on means vectors and patterns of covariance matrices are too elaborate to be used in practice, the authors propose in this paper the use of near-exact distributions for these statistics. These are asymptotic distributions which are built using a different concept in approximating distributions which combines an adequately developed decomposition of the c.f. of the statistic or of its logarithm, most often a factorization, with the action of keeping then most of this c.f. unchanged and replacing the remaining smaller part by an adequate asymptotic approximation [5, 6]. All this is done in order to obtain a manageable and very well-fitting approximation, which may be used to compute near-exact quantiles or values. These distributions are much useful in situations where it is not possible to obtain the exact distribution in a manageable form and the common asymptotic distributions do not display the necessary precision. Near-exact distributions show very good performances for very small samples, and when correctly developed for statistics used in Multivariate Analysis, near-exact distributions display a sharp asymptotic behavior both for increasing sample sizes and for increasing number of variables.

In Sections 3–7, near-exact distributions are obtained using different techniques and results, according to the structure of the exact distribution of the statistic.

In order to study, in each case, the proximity between the near-exact distributions developed and the exact distribution, we will use the measure withwhere represents the l.r. statistic, is the exact c.f. of , is the near-exact c.f., and , , , and are the exact and near-exact c.d.f.’s of and .

This measure is particularly useful, since in our cases we do not have the exact c.d.f. of or in a manageable form, but we have both the exact and near-exact c.f.’s for .

#### 2. The Likelihood Ratio Test for the Simultaneous Test of Equality of Means and the Circularity of the Covariance Matrix

Let , where . Then, for a sample of size , the th power of the l.r. statistic to test the null hypothesisis where , is the maximum likelihood estimator (m.l.e.) of , , where is the matrix with running elementwith , and where is the th diagonal element of , andwith , where is the vector of sample means.

This test statistic was derived by [1, sec. 5.2], where the expression for the l.r. test statistic has to be slightly corrected.

According to [1], where are a set of independent r.v.’s.

From this fact we may write the c.f. of as

By adequately handling this c.f., the exact distribution of is obtained in [4] as a Generalized Integer Gamma (GIG) distribution (see [7] for the GIG distribution), since we may writefor

A popular asymptotic approximation for the distribution of is the chi-square asymptotic distribution with a number of degrees of freedom equal to the difference of the number of unknown parameters under the alternative hypothesis and the number of parameters under the null hypothesis, which gives for , for in (8), a chi-square asymptotic distribution with degrees of freedom. Although this is a valid approximation for large sample sizes, in practical terms, this approximation is somewhat useless given the fact that it gives quantiles that are much lower than the exact ones, as it may be seen from the quantiles in Table 1, namely, for small samples or when the number of variables involved is somewhat large.