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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 most in agreement with the data configuration. For instance, for the bivariate data, y1,y2, if the quadratic coordinate system is sought, a variable z is defined as given below: with the coefficients being set up so that the variance of z is a maximum amongst all such quadratic functions of y1 and y2.
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
Multidimensional scaling (MDS) is a generic term for a class of techniques or methods which attempt to construct a low-dimensional geometrical representation of the proximity matr
literature review of latin square design.
Collective risk models : The models applied to insurance portfolios which do not create direct reference to the risk characteristics of individual members of the portfolio when des
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Range is the difference between the largest and smallest observations in the data set. Commonly used as an easy-to-calculate measure of the dispersion in the set of observations b
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
Intervention analysis in time series : The extension of the autoregressive integrated moving average models applied to time series permitting for the study of the magnitude and str
Data which occur when failure period is recorded which are dependent. Such type of data can arise in number contexts, for instance, in epidemiological cohort studies in which th
Longini Koopman model : In epidemiology the model for primary and secondary infection, based on the classification of the extra-binomial variation in an infection rate which might
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