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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.
Length-biased data is a data which arise when the probability that an item is sampled is proportional to its own length. A main example of this situation occurs in the renewal the
Probit analysis is the technique most commonly employed in the bioassay, specifically toxicological experiments where the group of animals is subjected to known levels of a toxin
How large would the sample need to be if we are to pick a 95% confidence level sample: (i) From a population of 70; (ii) From a population of 450; (iii) From a population of 1000;
The generalization of the normal distribution used for the characterization of functions. It is known as a Gaussian process because it has Gaussian distributed finite dimensional m
The diagnostic tools or devices used to approach the closeness to the linearity of the non-linear model. They calculate the deviation of so-called expectation surface from the plan
Initial data analysis (IDA): The first phase in the examination of the data set which comprises number of informal steps including the following steps * checking the quality o
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
The measure of the degree to which the particular model differs from the saturated model for the data set. Explicitly in terms of the likelihoods of the two models can be defined a
Causality: The relating of the reasons to the effects they produce. Several investigations in medicine seek to establish the causal relations between the events, for instance, whi
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