Multivariate Techniques: Discriminant and Cluster Analyses
Identifying sources of groundwater contamination in a hard-rock aquifer system using multivariate statistical analyses and GIS-based geostatistical modeling techniques
... applying multivariate statistical analyses, trend identification and geostatistical modeling techniques in ...where multivariate statistical techniques are integrated with GIS-based ...
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An evaluation of cluster analysis and related multivariate techniques for operational research
... ordination method used was a metric stress minimization. procedure with no modifications[r] ...
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An evaluation of cluster analysis and related multivariate techniques for operational research
... analysis methods, and an investigation of the properties of ordination techniques. Case studies will be given, and a discussion of other applications, to shovr the[r] ...
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Multivariate analyses
... advocate discriminant analysis instead (we do not cover that in this ...univariate analyses should be undertaken only if the multivariate outcome is ...
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Discriminant analysis of multivariate time series using wavelets
... the techniques employed by the above-mentioned authors, the components of the 12-lead ECG signal are treated as if they were independent of each ...12-component multivariate time ...the discriminant ...
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Genotype imputation based on discriminant and cluster analysis
... In addition to the obvious advantage of allowing standard statistical methods of complete data analysis, applying Imputation in row data also has the important advantage of allowing the use of row information available ...
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A Cluster Elastic Net for Multivariate Regression
... first cluster has popcorn, hamburger, french fires, bottled water, appetizers, and a chicken ...second cluster consists of hot dogs, craft beer and misc sides, which represents a group of higher selling ...
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Use of multivariate discriminant methodologies in the analysis of phenotypic and genomic data of cattle
... Marker selection In the present research, the DAM approach to develop a GWAS for RCI was proposed. The algorithm was able to overcome two of the most important drawbacks that affect the traditional single SNP regression ...
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Comparison of multivariate methods in group/cluster identification
... (PCA), Discriminant Analysis and ...cases. Discriminant Analysis was applied ...biochemical analyses and showed that Urea is indeed the best predictor, followed by Creatinine and then Serum Uric ...
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An Application of Artificial Intelligent Neural Network and Discriminant Analyses on Credit Scoring
... Appropriate predictor variables selection is one of the conditions for successful credit scoring models development. This study reviews several considerations regarding the selection of the predictor variables. Moreover, ...
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Multivariate Analysis Techniques in Environmental Science
... between cluster analyses and your individual data ...a cluster analysis on both the rows and columns of your matrix, followed by graphing the two dendrograms simultaneously, adjacent to a ...
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Multivariate genome-wide analyses of the well-being spectrum
... any multivariate GWAMA or GWAS model for which the per SNP model fit can be expressed in terms of an AICc fit ...in multivariate GWAMA over univariate GWAMA for traits genetically correlated above ...on ...
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Multivariate analyses of selected mechanical properties of dry bean grain
... able to provide energy over a long period of time by being slowly released into the bloodstream to provide sustained energy levels (USDA, 2012). Some of the mechanical properties are becoming in- reasingly important for ...
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Measuring social capital through multivariate analyses for the IQ SC
... Step Cluster method based on the Euclidian distance and the centroid criteria as the criteria for aggregate ...item, discriminant analysis was used to validate the cluster analysis in an attempt to ...
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Multivariate analyses to determine fungicide efficacy on Ecuadorian bananas for consumption
... study, multivariate statistical process control was adjusted to a fungicide efficacy evaluation case considering multiple data tables from different locations and years at the same ...the multivariate ...
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COSIMA data analysis using multivariate techniques
... When the number of parameters grows, the interpretation gets even harder, the curse of dimensionality. This problem has been investigated and quantitative bounds for the validity of estimates of principal components have ...
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Visualizing non-hierarchical and hierarchical cluster analyses with clustergrams
... of cluster analysis is to cluster observations into ...(e.g. discriminant analysis) training data with correctly classified observations from which one may learn the group assignments are not ...
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Multiple outlier detection and cluster analysis of multivariate normal data
... Pr{At least one clean p + 1 subset} = 1 − (1 − (1 − κ) p+1 ) m . (4.20) We consider this probability again later. 4.7.3 Comments Fast-MCD as described in [24] contains two other relatively mundane modifications to the ...
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Discriminant non-stationary signal features’ clustering using hard and fuzzy cluster labeling
... and discriminant features of each class, or employing complex and nonlinear feature ...clustering techniques, proposes an algorithm to adaptively identify the feature vectors according to their importance ...
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An Investigation of Food Quality and Oil Stability Indices of Muruku by Cluster Analysis and Discriminant Analysis
... to 15.73 mg KOH/g and 2.42 to 38.59 mg MAD eq/kg, respectively. M2 (sample from a market) had the highest PV and AV whilst M1 (sample from the street vendor) showed the highest PAV and TBA values. Compared to other ...
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