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Power Estimation in Multivariate Analysis of Variance [PDF]
Tutorials in Quantitative Methods for Psychology, 2007 Power is often overlooked in designing multivariate studies for the simple reason that it is believed to be too complicated. In this paper, it is shown that power estimation in multivariate analysis of variance (MANOVA) can be approximated using a F ...
Jean François Allaire, Sylvain Chartier
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Bayesian Estimation in Multivariate Analysis
The Annals of Mathematical Statistics, 1965 Abstract : The Bayes approach to Multivariate Analysis taken previously by Geisser and Cornfield (JRSS Series B, 1963 No. 2, pp. 368-376) is extended and given a more comprehensive treatment. Posterior joint and marginal densities are derived for vector means, linear combinations of means; simple and partial variances; simple, partial and multiple ...
S. Geisser
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Multi-Platform Multivariate Regression with Group Sparsity for High-Dimensional Data Integration [PDF]
EntropyHigh-dimensional regression with multivariate responses poses significant challenges when data are collected across multiple platforms, each with potentially correlated outcomes.
Shanshan Qin +3 more
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Repeated measures discriminant analysis using multivariate generalized estimation equations
Statistical Methods in Medical Research, 2021 Discriminant analysis procedures that assume parsimonious covariance and/or means structures have been proposed for distinguishing between two or more populations in multivariate repeated measures designs. However, these procedures rely on the assumptions of multivariate normality which is not tenable in multivariate repeated measures designs which ...
Anita Brobbey +6 more
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Model and distribution uncertainty in multivariate GARCH estimation: A Monte Carlo analysis [PDF]
Computational Statistics & Data Analysis, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
ROSSI, EDUARDO, Spazzini Filippo
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Estimation of the Number of “True” Null Hypotheses in Multivariate Analysis of Neuroimaging Data
NeuroImage, 2001 The repeated testing of a null univariate hypothesis in each of many sites (either regions of interest or voxels) is a common approach to the statistical analysis of brain functional images. Procedures, such as the Bonferroni, are available to maintain the Type I error of the set of tests at a specified level.
Turkheimer, F E, Smith, C B, Schmidt, K
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On the estimation of the number of components in multivariate functional principal component analysis [PDF]
Communications in statistics. Simulation and computation, 2023 Happ and Greven developed a methodology for principal components analysis of multivariate functional data observed on different dimensional domains. Their approach relies on an estimation of univariate functional principal components for each univariate ...
Steven Golovkine +3 more
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Risk management via contemporaneous and temporal dependence structures with applications
MethodsX, 2021 This paper presents the estimation methods of the Bayesian Graphical Vector Auto-regression with and without innovations such as external regressors (BG-VAR(X)) and Bayesian Graphical Systems Equation Modelling with and without exogenous variables (BG ...
Emmanuel Senyo Fianu +2 more
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Multivariate analysis of curvature estimators [PDF]
Computer-Aided Design and Applications, 2016 ABSTRACTPrincipal curvature is one of the defining features of surfaces studied in differential geometry. While well-defined and easy to evaluate for smooth surfaces, it cannot be evaluated exactly if the surface is represented by a polygon mesh, unless some special conditions apply. Nevertheless, estimating the curvature of a surface mesh is a crucial
Libor Váša +2 more
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Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study. [PDF]
PLoS ONE, 2015 In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between ...
Binod Neupane, Joseph Beyene
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