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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.
Mendelian randomization is the term applied to the random assortment of alleles at the time of gamete formation, a process which results in the population distributions of genetic
Johnson-Neyman technique: The technique which can be used in the situations where analysis of the covariance is not valid because of the heterogeneity of slopes. With this method
Matching distribution is a probability distribution which arises in the following manner. Suppose that the set of n subjects, numbered 1; . . . ; n respectively, are arranged in
Computer-intensive methods : The statistical methods which require almost identical computations on the data repeated number of times. The term computer intensive is, certainly, a
Briefly explain the importance of forecasting for managers?
The model for data containing continuous and categorical variables both.The categorical data are summarized by the contingency table and their marginal distribution, 182by the mult
Multi-hit model is the model for a toxic response which results from the random occurrence of one or the more fundamental biological events. A response is supposed to be induced o
explain the graphical method of measure of central tendency
what is operational gaining
Quantile regression is an extension of the classical least squares from estimation of the conditional mean models to the estimation of the variety of models for many conditional q
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