Search results for "Multivariate normal distribution"
showing 10 items of 47 documents
On Mardia’s Tests of Multinormality
2004
Classical multivariate analysis is based on the assumption that the data come from a multivariate normal distribution. The tests of multinormality have therefore received very much attention. Several tests for assessing multinormality, among them Mardia’s popular multivariate skewness and kurtosis statistics, are based on standardized third and fourth moments. In Mardia’s construction of the affine invariant test statistics, the data vectors are first standardized using the sample mean vector and the sample covariance matrix. In this paper we investigate whether, in the test construction, it is advantageous to replace the regular sample mean vector and sample covariance matrix by their affi…
Statistical Techniques for Validation of Simulation and Analytic Stochastic Models
2014
In this paper, we consider the problem of statistical validation of multivariate stationary response simulation and analytic stochastic models of observed systems (say, transportation or service systems), which have p response variables. The problem is reduced to testing the equality of the mean vectors for two multivariate normal populations. Without assuming equality of the covariance matrices, it is referred to as the Behrens–Fisher problem. The main purpose of this paper is to bring to the attention of applied researchers the satisfactory tests that can be used for testing the equality of two normal mean vectors when the population covariance matrices are unknown and arbitrary. To illus…
Elliptically Symmetric Distributions: A Review of Achieved Results and Open Issues
2005
The spherically and elliptically symmetrical distributions are used in different statistical areas for different purposes such as the description of multivariate data, in order to find alternatives to the normal distribution in multinormality tests and in the creation of statistical models in which the usual assumption of normality is not realistic. Some achieved results, open issues and some proposals for their use in applications, especially in the financial area, are here presented.
A Synthetic Approach to Multivariate Normal Clustering
1982
Two methods have been suggested for grouping together observations originated from the same multivariate normal distributions, both based on a maximum-likelihood (ML) estimation, but leading to different conclusions. In this paper we compare the use of Day’s approach and that of Scott and Symons in clustering procedures from the theoretical and computational point of view. Based on this comparison, we suggest an approach unifying those approaches. The workability of the approach will be verified by numerical experiments.
Two-stage adaptive designs with correlated test statistics.
2005
When performing a trial using an adaptive sequential design, it is usually assumed that the data for each stage come from different units; for example, patients. However, sometimes it is not possible to satisfy this condition or to check whether it is satisfied. In these cases, the test statistics and p-values of each stage may be dependent. In this paper we investigate the type I error of two-stage adaptive designs when the test statistics from the stages are assumed to be bivariate normal. Analytical considerations are performed under the restriction that the conditional error function is constant in the continuation region. We show that the decisions can become conservative as well as an…
Kullback-Leibler distance as a measure of the information filtered from multivariate data
2007
We show that the Kullback-Leibler distance is a good measure of the statistical uncertainty of correlation matrices estimated by using a finite set of data. For correlation matrices of multivariate Gaussian variables we analytically determine the expected values of the Kullback-Leibler distance of a sample correlation matrix from a reference model and we show that the expected values are known also when the specific model is unknown. We propose to make use of the Kullback-Leibler distance to estimate the information extracted from a correlation matrix by correlation filtering procedures. We also show how to use this distance to measure the stability of filtering procedures with respect to s…
“Anti-Bayesian” flat and hierarchical clustering using symmetric quantiloids
2017
A myriad of works has been published for achieving data clustering based on the Bayesian paradigm, where the clustering sometimes resorts to Naive-Bayes decisions. Within the domain of clustering, the Bayesian principle corresponds to assigning the unlabelled samples to the cluster whose mean (or centroid) is the closest. Recently, Oommen and his co-authors have proposed a novel, counter-intuitive and pioneering PR scheme that is radically opposed to the Bayesian principle. The rational for this paradigm, referred to as the “Anti-Bayesian” (AB) paradigm, involves classification based on the non-central quantiles of the distributions. The first-reported work to achieve clustering using the A…
Statistical validation of simulation models of observable systems
2003
In this paper, for validating computer simulation models of real, observable systems, an uniformly most powerful invariant (UMPI) test is developed from the generalized maximum likelihood ratio (GMLR). This test can be considered as a result of a new approach to solving the Behrens‐Fisher problem when covariance matrices of two multivariate normal populations (compared with respect to their means) are different and unknown. The test is based on invariant statistic whose distribution, under the null hypothesis, does not depend on the unknown (nuisance) parameters. The sample size and threshold of the UMPI test are determined from minimization of the weighted sum of the model builder's risk a…
Discrete Time Portfolio Selection with Lévy Processes
2007
This paper analyzes discrete time portfolio selection models with Lévy processes. We first implement portfolio models under the hypotheses the vector of log-returns follow or a multivariate Variance Gamma model or a Multivariate Normal Inverse Gaussian model or a Brownian Motion. In particular, we propose an ex-ante and an ex-post empirical comparisons by the point of view of different investors. Thus, we compare portfolio strategies considering different term structure scenarios and different distributional assumptions when unlimited short sales are allowed.
The size of Simes’ global test for discrete test statistics
1999
Abstract To increase the power of the Bonferroni–Holm procedure several modified Bonferroni procedures have been proposed (for example, Hochberg, 1988. Biometrika 75, 800–802; Hommel, 1988. Biometrika 75, 383–386), which are based on Simes’ global test (Simes, 1986. Biometrika 73, 751–754). By several simulation studies which, in particular, considered multinormal test statistics, it has been suggested that the Simes test is a level α test. However, an exact proof exists for only few situations one of them assuming independence of test statistics. We studied the behaviour of Simes’ test for discrete test statistics. Due to discreteness one can expect more conservative decisions whereas depe…