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This is extension of the EM algorithm which typically converges more slowly than EM in terms of the iterations but can be much faster in the whole computer time. The general idea of the algorithm is to replace M-step of each EM iteration with the sequence of S >1conditional or constrained maximization or the CM-steps, each of which maximizes the expected complete-data log-likelihood found in the previous E-step subject to constraints on parameter of interest, θ, where the collection of all the constraints is such that the maximization is over the full parameter space of θ. Because the CM maximizations are over the smaller dimensional spaces, many times they are simpler, faster and more reliable than corresponding full maximization known in the M-step of the EM algorithm.
the problem that demonstrates inference from two dependent samples uses hypothetical data from TB vaccinations and the number of new cases before and after vaccinations for cases o
Respondent-driven sampling (RDS ): The form of snowball sampling which starts with the recruitment of the small number of people in the target population to serve as the seeds. Aft
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4-13. Students in a management science class have just received their grades on the first test. The instructor has provided information about the first test grades in some previou
re-reference all these indexes
Ordinal variable is a measurement which allows a sample of the individuals to be ranked with respect to some characteristic but where differences at different points of the scale
Asymmetric proximity matrices : Proximity matrices in which the non-diagonal elements, in the ith row and jth column and the jth row and ith column, are not essentially equal. Exam
Build-Rite construction has received favorable publicity from guest appearances on a public TV home improvement program. Public TV programming decisions seem to be unpredictable, s
The phrase first spoken by one of the witches in Macbeth. Now this is used to describe the exponential rise in the number of possible locations in the multivariate space as dimensi
An approach to decrease the size of very large data sets in which the data are first 'binned' and then statistics such as the mean and variance/covariance are calculated on each bi
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