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Prior distributions: The probability distributions which summarize the information about a random variable or parameter known or supposed at a given time instant, prior to attaining further information from the empirical data. It is used almost entirely within the context of Bayesian inference. In any specific study a variety of such kind of distributions might be assumed. For instance, reference priors represent the minimal prior information; clinical priors are used to formalize the opinion of well-informed specific individuals, frequently those taking part in the trial themselves. Lastly, sceptical priors are used when the large treatment differences are considered unlikely.
Grade of membership model: This is the general distribution free method for the clustering of the multivariate data in which only categorical variables are included. The model ass
A construction for events that happen in some planar area a, consisting of the series of 'territories' each of which comprises of that part of a closer to the particular event xi t
How do I report the results in the table?
Normal approximation : Normal distributions which approximate other distributions; such as, a normal distribution with the mean np and variance np(1 - p) which acts as an approxima
Jelinski Moranda model is t he model of software reliability which supposes that failures occur according to the Poisson process with a rate decreasing as more faults are diagnos
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
In an experiment, power is a function of 1. The number of variables being measured and the beta level 2. The effect size, internal validity and the beta level 3. The number of part
L'Abbe ´ plot is often used in the meta-analysis of the clinical trials where the result is the binary response of it. The event risk (number of events/number of the patients in a
Ordination is the procedure of reducing the dimensionality (that is the number of variables) of multivariate data by deriving the small number of new variables which contain much
Longitudinal data : The data arising when each of the number of subjects or patients give rise to the vector of measurements representing same variable observed at the number of di
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