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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.
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The approach to statistics based on a frequency view of probability in which it is supposed that it is possible to consider an in?nite sequence of the independent repetitions of th
Lie factor : A measure suggested by Tufte for judging the honesty of the graphical presentation of data. Which can be calculated as follows The values close to one are desir
A construction for events that happen in some planar area a, consisting of the series of 'territories' each of which comprises of that part of a closer to the particular event xi t
Regression diagnostics is the process designed to investigate the suppositions underlying particular forms of regression examination, for instance, homogeneity of variance, norma
Chebyshev's inequality: A statement about the proportion of the observations which fall within some number of the standard deviations of the mean for any of the probability distri
Interior analysis is the term now and again applied to analysis carried out on the fitted model in regression problem. The basic target of such analyses is the identification of
Invariant transformations to combine marginal probability functions to form multivariate distributions motivated by the need to enlarge the class of multivariate distributions beyo
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 >
Ordination is the procedure of reducing the dimensionality (that is the number of variables) of multivariate data by deriving the small number of new variables which contain much
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