This paper provides a tutorial introduction to the role of the time-delay or the interactor matrix in multivariate minimum variance control. Minimum variance control gives the lowest achievable output variance and thus serves as a useful benchmark for performance assessment. One of the major drawbacks of the multivariate minimum variance benchmark is the need for apriori knowledge of the multiv...

We propose two methods for estimating multivariate Tobit Confirmatory Factor Analysis (TCFA) with covariates, one from Bayesian and another from likelihood based perspectives. TCFA is particularly useful in analysis of multivariate data with censored information. In contrast with previous developments that utilize Monte Carlo simulations for maximum likelihood estimation, an exact EM-type algor...

Recent improvements in imaging photoelectron spectroscopy enhance lateral and vertical characterization of heterogeneous samples at the cost of increasing complexity in the XPS data sets acquired. These imaging data sets require more sophisticated analysis methods than visual inspection if the data are to be interpreted effectively. Multivariate analysis (MVA) methods are increasingly utilized ...

this experiment was conducted to investigate and evaluate the phenotypic and genotypic diversity of a population including of 94 doubled-haploids lines, their parents and five iranian local bread wheat (triticum aestivum l.) cultivars. thirteen agronomic and morphologic traits were recorded including; grain yield per unit area, harvest index, spike length, seed number per spike, spikelet densit...

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data— data from observational arrays, from satellites or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression (LR) at the base, followed by principal component analysis (PCA), and finally canonical corre...