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Ignorability: The missing data mechanism is said to be ignorable for likelihood inference if (1) the joint likelihood for the responses of the interest and missing data indicators can be decomposed into the two separate components (containing parameters of the main interest and the parameters of the missingness mechanism,) and (2) the parameters for each component are distinct in the sense that there are no parameter restrictions across components. The component for the missingness mechanism can then be unnoticed in statistical inference for the parameters of interest. Ignorability follows if the missing values are missing completely at random or missing at random and the parameters are distinct.
Likert scales is often used in the studies of attitudes in which the raw scores are based on the graded alternative responses to each of a series of queries. For instance, the sub
Difference b/w historigram and histogram
For a career woman, wearing lipstick has become an integral part of her daily life. It is not unusual for a woman to look for a lipstick that will stay on her lips and not smudge
Longini Koopman model : In epidemiology the model for primary and secondary infection, based on the classification of the extra-binomial variation in an infection rate which might
Multiple comparison tests : Procedures for detailed examination of the differences between a set of means, generally after a general hypothesis that they are all equal has been rej
Perturbation theory : The theory useful in assessing how well a specific algorithm or the statistical model performs when the observations suffer less random changes. In very commo
The graph for Partial Autocorrelation Function for RES1 shows that there is no autocorrelation even though there are alternating spikes because they fall inside the 5% significance
Collective risk models : The models applied to insurance portfolios which do not create direct reference to the risk characteristics of individual members of the portfolio when des
what are all the organs
The probability distribution which is a linear function of the number of component probability distributions. This type of distributions is used to model the populations thought to
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