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The term used for the estimation of the misclassification rate in the discriminant analysis. Number of techniques has been proposed for two-group situation, but the multiple-group situation has rarely been addressed. The easiest procedure is the resubstitution technique, in which the training data are classified using the estimated classification rule and proportion incorrectly placed used as the estimate of misclassification rate. This technique is known to have a large optimistic bias, but it has the benefit that it can be applied to the multigroup problems with no modification required. An alternative technique is the leave one out estimator, in which each of the observation in turn is removed from the data and the classification rule recomputed using remaining data. The proportion improperly classified by the procedure will have reduced bias compared to resubstitution technique. This method can also be implied to the multi group problem with no modification but it has the large amount of variance.
Indirect least squares: An estimation technique used in the fitting of structural equation models. Commonly least squares are first used to estimate reduced form parameters. Usi
Kaiser's rule is the rule frequently used in the principal components analysis for selecting the suitable the number of components. When the components are derived from correlati
Cauchy distribution : The probability distribution, f (x), can be given as follows where α is the position of the parameter (median) and the beta β a scale parameter. Moments
The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if nR2 > MTB >
Omitted covariates is a term generally found in the connection with regression modelling, where the model has been incompletely specified by not including significant covariates.
Helmert contrast is the contrast often used in analysis of the variance, in which each level of a factor is tested against average of the remaining levels. So, for instance, if th
Bayes factor : A summary of evidence for the modelM1 against the another modelM0 provided by the set of data D, which can be used in the model selection. Given by the ratio of post
A term usually used for unobserved individual heterogeneity. Such variation is of main concern in the medical statistics particularly in the analysis of the survival times where ha
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how to describe association between quantitative and categorical variables
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