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The Expectation/Conditional Maximization Either algorithm which is the generalization of ECM algorithm attained by replacing some of the CM-steps of ECM which maximize the constrained expected complete-data log-likelihood, with steps that maximize correspondingly constrained real likelihood. The algorithm can have substantially faster convergence rate than either the EM algorithm or ECM measured using either the number of iterations or actual computer time. There are two reasons for this enhancement. First, in some of the ECME's maximization steps the actual likelihood is being conditionally maximized, rather than the current approximation to it as with EM and ECM. Second,
ECME permits faster converging numerical techniques to be used on only those constrained maximizations where they are most efficacious.
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Behrens Fisher problem : The difficulty of testing for the equality of the means of the two normal distributions which do not have the equal variance. Various test statistics have
Radical statistics group : The national network of the social scientists in United Kingdom committed to the critique of statistics as taken in use in the policy making procedure. T
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
Evaluate the following statistical arguments. Begin by identifying the sample, population, and the property which is being investigated. Do these arguments sound acceptable? Would
Kappa coefficient : The chance corrected index of the agreement between, for instance, judgements and diagnoses made by the two raters. Calculated as the ratio of the noticed exces
A value related with the square matrix which represents sums and products of its elements. For instance, if the matrix is then the determinant of A (conventionally written as
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This is the theorem which states that if the error terms in a multiple regression have the same variance and are not corrected, then the estimators of the parameters in the model p
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