March 05, 2013

Multivariate Distributions (Advanced Data Analysis from an Elementary Point of View)

Reminders about multivariate distributions. The multivariate Gaussian distribution: definition, relation to the univariate or scalar Gaussian distribution; effect of linear transformations on the parameters; plotting probability density contours in two dimensions; using eigenvalues and eigenvectors to understand the geometry of multivariate Gaussians; conditional distributions in multivariate Gaussians and linear regression; computational aspects, specifically in R. General methods for estimating parametric distributional models in arbitrary dimensions: moment-matching and maximum likelihood; asymptotics of maximum likelihood; bootstrapping; model comparison by cross-validation and by likelihood ratio tests; goodness of fit by the random projection trick.

Reading: Notes, chapter 14

Advanced Data Analysis from an Elementary Point of View

Posted at March 05, 2013 10:30 | permanent link

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