The analysis of crossover designs assuming i.i.d. errors leads to biased variance estimates whenever the true covariance structure is not spherical. As a result, the OLS F-Test for treatment differences is not valid. Bellavance et al. (Biometrics 52:607-612, 1996) use simulations to show that a modified F-Test based on an estimate of the within subjects covariance matrix allows for nearly unbia...

Repeated measures analyses of variance are the method of choice in many studies from experimental psychology and the neurosciences. Data from these fields are often characterized by small sample sizes, high numbers of factor levels of the within-subjects factor(s), and nonnormally distributed response variables such as response times. For a design with a single within-subjects factor, we invest...

s2i;a1 ,s2i;b3 and s 2 i;a1 b3 , variances and covariance of random effects in parameters a1 and b3 at plot level; s2ij;a1 ,s2ij;b3 and s2ij;a1 b3 , variances and covariance of random effects in parameters a1 and b3 at tree level; σ , residual variance; δ parameter of power function. Note that the σ of the mixed-effects model must be multiplied by g = d when applied (variance obtained from ordi...

In this paper we use the Moore-Penrose inverse in the case of a close to singular and ill-conditioned, or singular variance-covariance matrix, in the classic Portfolio Selection Problem. In this way the possible singularity of the variance-covariance matrix is tackled in an efficient way so that the various application of the Problem to benefit from the numerical tractability of the Moore-Penro...

We introduce covariance reducing models for studying the sample covariance matrices of a random vector observed in different populations. The models are based on reducing the sample covariance matrices to an informational core that is sufficient to characterize the variance heterogeneity among the populations. They possess useful equivariance properties and provide a clear alternative to spectr...

Hierarchical linear models with a block circular covariance structure are considered. Sufficient conditions for obtaining explicit and unique estimators for the variance-covariance components are derived. Different restricted models are discussed and maximum likelihood estimators are presented.

This paper is concerned with methods for analyzing quantitative, noncategorical profile data, e.g., a battery of tests given to individuals in one or more groups. It is assumed that the variables have a multinormal distribution with an arbitrary variance-covariance matrix. Approximate procedures based on classical analysis of variance are presented, including an adjustment to the degrees of fre...

A Monte Carlo study was used to evaluate the effects of reductions in posttest variance on several methods for detecting predictors of change in a two-wave design. When the predictor was dichotomous, the analysis of covariance approach was compared to the analysis of variance on difference scores. For a continuous predictor, partial correlations, difference score correlations with the predictor...

The analysis of crossover designs assuming i.i.d. errors leads to biased variance estimates whenever the true covariance structure is not spherical. As a result, the OLS F-Test for treatment differences is not valid. Bellavance et al. (Biometrics 52:607-612, 1996) use simulations to show that a modified F-Test based on an estimate of the within subjects covariance matrix allows for nearly unbia...

In this thesis, I propose and implement a generalized Gaussian methodology to accomodate the asymmetry and kurtosis of financial returns data, features not captured by standard Gaussian methods. The methodology extends from Gaussian methods with one variance-covariance matrix by estimating different variance-covariance matrices to characterize the differential risk exposures in long and short p...