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Multiple imputation: The Monte Carlo technique in which missing values in the data set are replaced by m> 1 simulated versions, where m is usually small (say 3-10). Each of simulated complete datasets is analyzed by the technique appropriate to the investigation at hand, and results are later combined to generate estimates, confidence intervals etc. The imputations are created by the Bayesian approach which needs specification of the parametric model for the complete data and, if necessary, a model for mechanism by which data become missing.
Hear also required is a prior distribution for unknown model parameters. Bayes' theorem is taken in use to simulate m independent samples from the conditional distribution of the missing values provided the observed values. In most of the cases special computation techniques such as Markov chain Monte Carlo methods will be required.
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Quincunx is the device used by Galton to illustrate his lectures, which is shown in the Figure. It had a glass face and a funnel at its top. The shot was passed through funnel an
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This is an alternative to the Newton-Raphson technique for optimization (finding out the minimum or the maximum) of some function, which includes replacing the matrix of second der
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Dear Experts, Please note that I''m doing a PhD in Business management under the title: Technology transfer and competitive advantage in Qatar oil and gas companies. It is a quant
Correlation matrix : A square, symmetric matrix with the rows and columns corresponding to the variables, in which the non diagonal elements are correlations between the pairs of t
Probability distribution : For the discrete random variable, a mathematical formula which provides the probability of each value of variable. See, for instance, binomial distributi
Incidental parameter problem is a problem which sometimes occurs when the number of parameters increases in the tandem with the number of observations. For instance, models for pa
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