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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.
Generalized poisson distribution: The probability distribution can be defined as follows: The distribution corresponds to the situation in which the values of the rand
Assume that a population is normally distributed with a mean of 100 and a standard deviation of 15. Would it be unusual for the mean of a sample of 20 to be 115 or more?
relevancy of time series in business management
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
Data theory is anxious with how observations are transformed into data which can be analyzed. Data are thus viewed as the theory laden in the sense that the observations can be giv
Prospective study : The studies in which individuals are followed-up over the period of time. A general example of this type of investigation is where the samples of individuals ar
regression line drawn as Y=C+1075x, when x was 2, and y was 239, given that y intercept was 11. calculate the residual
Poisson regression In case of Poisson regression we use ηi = g(µi) = log(µi) and a variance V ar(Yi) = φµi. The case φ = 1 corresponds to standard Poisson model. Poisson regre
The functions of the data and the parameters of interest which can be brought in use to conduct inference about the parameters when full distribution of the observations is unknown
Incubation period is the time elapsing amongs the receipt of infection and the appearance of the symptoms. The length of the incubation time period depends on the disease, ranging
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