Multivariate statistical models

A widely used approach for estimation of source contributions at receptor sites is receptor modelling [31, 32], In receptor models, source contributions are estimated based on the measurements of various chemical constituents in a sufficiently large number of ambient PM samples, often filter samples that are collected during 24 h. Depending on the available knowledge about the main sources, CMB or multivariate statistical models can be applied CMB requires a priori knowledge of the chemical profile of all relevant sources, i.e. the percentage of the chemical... 

Fahrmeir, L., Tutz, G. Multivariate Statistical Modelling based on Generalized Linear Models, Springer, Berlin, Heidelberg, New York, 1994... 

S. Yoon and J.F. MacGregor. Fault diagnosis with multivariate statistical models part I using steady state fault signature. Journal of Process Control, 11 387-400, 2001. 

Quantitative Structure Activity Relationships (QSAR) and Modeling Society. URL http //www.ndsu.nodak.edu/qsar soc/index.htm. Classical QSAR, multivariate statistical modeling, molecular modeling, computer-aided drug design, and environmental chemistry. 

Two fundamentally different statistical approaches to biomarker selection are possible. With the first, experimental data can be used to construct multivariate statistical models of increasing complexity and predictive power - well-known examples are Partial Least Square Discriminant Analysis (PLS-DA) (Barker Rayens, 2003 Kemsley, 1996 Szymanska et al., 2011) or Principal Component Linear Discriminant Analysis (PC-LDA) (Smit et al., 2007 Werf et al., 2006). Inspection of the model coefficients then should point to those variables that are important for class discrimination. As an alternative, univariate statistical tests can be... 

Cao Xue-Chun 2010. Hydrochemical classification and identification for groundwater system by using integral multivariate statistical models. Journal of China Coal SocietyJoumaL 14(8) 18-20. 

In Section 2, this paper proposes a modular OSS designed for multi-product processes. While the primary contribution is the prototype implementation of the strategy, certain elements of this approach are novel contributions. The latter include the application of enterprise and process model based data-warehouse and synergistic integration of multivariate statistical models to the operator interfaces. This concept is illustrated in Section 3, where the prototype of the proposed OSS is applied to high-density polyethylene (MDPE, HOPE) plant. 

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