Discrimination and classification (2/2)

WeekCourse ContentCourse VideoCourse Download
Week 1IntroductionWatch OnlineMP4 Download
Week 1Aspects of multivariate analysis
-introduction
-review of linear algebra and matrices
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Week 1Matrix algebra and random vectors
-random vectors
-distance
-sample geometry
-random sampling of sample mean vector and covariance matrix
-generalized variance
-matrix operations of sample values
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Week 2Multivariate normal distribution
-density and properties
-sampling from multivariate normal and MLE
-sampling distribution and large sample behavior of X and S
-assessing the assumption of normality
-transformation to near normality
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Week 3Inferences about a mean vector
-inference for a normal population mean
-Hotelling's T2 and likelihood ratio test
-confidence regions and simultaneous comparisons of component means
-large sample inferences about a population mean vector
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Week 4Comparisons of several multivariate means
-paired comparisons and repeated measures design
-comparing mean vectors from two populations
-comparing several multivariate population means (one-way MANOVA)
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Week 5Principal components
-introduction
-population principal components
-summarizing sample variation by principal components
-large sample inferences
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Week 6Factor analysis (1/2)
-introduction
-orthogonal factor model
-methods of estimation
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Week 8Factor analysis (2/2)
-factor rotation
-factor scores
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Week 10Clustering (1/2)
-introduction
-similarity measures
-hierarchical clustering methods
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Week 11Clustering (2/2)
-k-means clustering methods
-model-based clustering
-multidimensional scaling 
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Week 12Discrimination and classification (1/2)
-introduction
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Week 13Discrimination and classification (2/2)
-evaluation of classification functions
-classification with several populations
-fisher's method for discriminating among several populations
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Week 14Canonical correlation analysis (1/2)
-introduction
-canonical variates and canonical correlations
-interpreting the population canonical variables
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Week 15Canonical correlation analysis (2/2)
-sample canonical variates and sample canonical correlations
-sample descriptive measures of goodness-of-fit
-proportions of explained sample variance
-large sample inferences
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