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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.
Response feature analysis is the approach to the analysis of longitudinal data including the calculation of the suitable summary measures from the set of repeated measures on each
Harris and Stevens forecasting is the method of making short term forecasts in the time series which is subject to abrupt changes in pattern and the transient effects. Instances o
Regression diagnostics is the process designed to investigate the suppositions underlying particular forms of regression examination, for instance, homogeneity of variance, norma
A term commonly encountered in the application of the agglomerative hierarchical clustering techniques, where it refers to the 'tree-like' diagram illustrating the series of steps
Designs in which the information on main effects and low-order inter- actions are attained by running only the fraction of the complete factorial experiment and supposing that part
Matching coefficient is a similarity coefficient for data consisting of the number of binary variables which is often used in cluster analysis. It can be given as follows he
Quantalassay: The experiment in which the groups of subjects are exposed to the different doses of, generally, a drug, to which the particular number respond. Data from such type
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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
This term is sometimes used for the data collected in those longitudinal studies in which more than the single response variable is recorded for each subject on each occasion. For
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