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The method or technique for producing the sequence of parameter estimates that, under the mild regularity conditions, converges to maximum likelihood estimator. Of particular significance in the context of the incomplete data problems. The algorithm comprises of two steps, called as the E, or Expectation step and the M, or the Maximization step. In the previous, the expected value of log-likelihood conditional on the observed data and the current estimates of parameters are found. In the M-step, the function is maximized to provide the updated parameter estimates which increase the likelihood. The two steps are alternated until the convergence is attained. The algorithm might, in some cases, becoms very slow to converge. This is acronym for the Epidemiological, Graphics, Estimation and Testing of the program developed for the analysis of the data from studies in epidemiology. It can be made in use for logistic regression and models might include random effects to permit over dispersion to be modelled. The beta- binomial distribution can be fitted.
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
The more effective display than a number of other methods or techniques, for instance, pie charts and bar charts, for displaying the quantitative data which are labeled. An instanc
meaning,uses,shortcomings and drawbacks of vital statistics
Half-normal plot is a plot for diagnosing the model inadequacy or revealing the presence of outliers, in which the absolute values of, for instance, the residuals from the multipl
The graphical process most frequently used in the analysis of data from a two-by-two crossover design. For each of the subject the difference between the response variable values o
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Kalman filter : A recursive procedure which gives an estimate of the signal when only the 'noisy signal' can be observed. The estimate is efficiently constructed by putting the exp
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Unequal probability sampling is the sampling design in which the different sampling units in the population have different probabilities of being included in sample. The differing
Regression diagnostics is the process designed to investigate the suppositions underlying particular forms of regression examination, for instance, homogeneity of variance, norma
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