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Multivariate Analysis
Published in Shyam S. Sablani, M. Shafiur Rahman, Ashim K. Datta, Arun S. Mujumdar, Handbook of Food and Bioprocess Modeling Techniques, 2006
Most commonly, multivariate statistics are applied for:3Developing taxonomies or systems of classificationInvestigating promising approaches to conceptualize or group itemsGenerating hypothesesTesting hypotheses
Model-Based Clustering
Published in Sylvia Frühwirth-Schnatter, Gilles Celeux, Christian P. Robert, Handbook of Mixture Analysis, 2019
Cluster analysis – also known as unsupervised learning – is used in multivariate statistics to uncover latent groups suspected in the data or to discover groups of homogeneous observations. The aim is thus often defined as partitioning the data such that the groups are as dissimilar as possible and that the observations within the same group are as similar as possible. The groups forming the partition are also referred to as clusters.
A review of rail track degradation prediction models
Published in Australian Journal of Civil Engineering, 2019
Amir Falamarzi, Sara Moridpour, Majidreza Nazem
Multivariate statistics is a sub-division of statistical analysis that can analyse more than one dependent variable. Guler et al. (2011) developed a multivariate statistical model for the prediction of railway track geometry deterioration. In this research, a track section in Turkey was observed. Sleeper type, speed, curvature, rail length and the history of maintenance activities (e.g. tamping, rail welding and sleeper renewal) were considered as independent variables, and track geometry parameters were considered as dependent variables. Based on the results of this study, it was determined that rail length had an effect on the deterioration rate with a negative sign. In addition, when the maintenance activities decreased, the renewal activities increased (Guler, Jovanovic, and Evren 2011).