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Invariant transformations to combine marginal probability functions to form multivariate distributions motivated by the need to enlarge the class of multivariate distributions beyond the multivariate normal distribution and its related functions such as the multi- variate Student's t-distribution and the Wishart distribution. An example is Frank's family of bivariate distributions. (The word 'copula' comes from Latin and means to connect or join.) Quintessentially copulas are measures of the dependent structure of the marginal distributions and they have been used to model correlated risks, joint default probabilities in credit portfolios and groups of individuals that are exposed to similar economic and physical environments. Also used in frailty models for surveying.
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The plot of the number of cases of the disease against the time period. A large and sudden increase corresponds to an epidemic. The example of this is shown in the figure drawn bel
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Generalized principal components analysis: The non-linear version of the principal components analysis in which the goal is to determine the non-linear coordinate system which is
Bivariate boxplot : A bivariate analogue of boxplot in which the inner area contains 50%of the data, and a 'fence' helps to identify the potential outliers. Robust methods or techn
Barnard, George Alfred (1915^2002) : Born in Walthamstow in the east of London, Barnard achieved a scholarship to St. John's College, Cambridge, from where he graduated in the math
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Model is the description of the supposed structure of a set of observations which can range from a fairly imprecise verbal account to, more commonly, a formalized mathematical exp
Bayesian network : It is essentially an expert system in which the uncertainty is dealt with using the conditional probabilities and Bayes' Theorem. Formally such type of network c
In an experiment, power is a function of 1. The number of variables being measured and the beta level 2. The effect size, internal validity and the beta level 3. The number of part
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