For time series discrimination,the main idea behind the proposed approach is to use a variance/covariance criterion to strengthen or weaken aligned observations according to their contribution to the variability within and between classes. To this end, the classical variance/covariance expression is extended to a set of time series, as well as to a partition of time series.

The genetic and environmental factor structures of intellectual abilities in 5-year-old Dutch twins were examined. Six subtests of the RAKIT, a Dutch intelligence test, were administered to 209 twin pairs. The subtests were categorized as either verbal or nonverbal. The genetic covariance structure displayed a two-common factor structure including specific factors to account for subtest residua...

It is shown that maximum likelihood estimation of variance components from twin data can be parameterized in the framework of linear mixed models. Standard statistical packages can be used to analyze univariate or multivariate data for simple models such as the ACE and CE models. Furthermore, specialized variance component estimation software that can handle pedigree data and user-defined covar...

Many scientiic studies require a thorough understanding of the scaling characteristics of observed processes. We derive and justify a decomposition of the usual cross-covariance in terms of scale-by-scale wavelet cross-covariances, and provide an estimator of the wavelet cross-covariance at speciied lag. For jointly stationary but generally non-Gaussian linear processes, asymptotic results are ...

Symmetry and separability of spatial-temporal covariances are the main assumptions that are frequently taken for granted in most applications because of the simplicity of constructing covariance structure. However, many studies in environmental sciences show that real data have complex spatial-temporal dependency structures resulting from lack of symmetry or violation of other standard assumpti...

Addresses issues related to partial measurement in variance using a tutorial approach based on the LISREL confirmatory factor analytic model. Specifically, we demonstrate procedures for (a) using "sensitivity analyses" to establish stable and substantively well-fitting baseline models, (b) determining partially invariant measurement parameters, and (c) testing for the invariance of factor covar...

In this paper, we consider the high-dimensional consistencies of KOO methods for selecting response variables in multivariate linear regression with covariance structures. Here, structures are considered as (1) independent structure same variance, (2) different variances, and (3) uniform structure. A sufficient condition model selection consistency is obtained using a method under asymptotic fr...

In modern activities such as banking, homeland security, information transportation, telecommunication, etc., people work with large and high dimension data sets. But, the higher the dimension the higher the probability that outliers will be present in the data sets. The ability to detect outliers in high dimension multivariate data sets is a challenging task. In this circumstance, robust estim...