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Independent component analysis (ICA) is the technique for analyzing the complex measured quantities thought to be mixtures of other more fundamental quantities, into their fundamental components. Typical instances of the data to which ICA may be applied are given below:
* electroencephalogram (EEG) signal, which includes contributions from number of different brain regions,
* person’s height, which is determined by contributions from number of different genetic and environmental factors
Recurrence risk : Usually the probability that an individual experiences an event of interest given previous experience(s) of the event; for example, the probability of recurrence
Confounding: A procedure observed in some factorial designs in which it is impossible to differentiate between some main effects or interactions, on the basis of the particular d
Designs in which the information on main effects and low-order inter- actions are attained by running only the fraction of the complete factorial experiment and supposing that part
Data which occur when failure period is recorded which are dependent. Such type of data can arise in number contexts, for instance, in epidemiological cohort studies in which th
Multi co linearity is the term used in the regression analysis to indicate situations where the explanatory variables are related by a linear function, making the inference of the
Point scoring is an easy distribution free method which can be used for the prediction of a response which is a binary variable from the observations on several explanatory variab
Designs which permits two or more questions to be addressed in the investigation. The easiest factorial design is one in which each of the two treatments or interventions are p
Battery reduction : A common term for reducing the number of variables of the interest in a study for the purposes of study and perhaps later data collection. For instance, an over
Partial least squares is an alternative to the multiple regressions which, in spite of using the original q explanatory variables directly, constructs the new set of k regressor v
The biggest and smallest variate values among the sample of observations. Significant in various regions, for instance flood levels of the river, speed of wind and snowfall.
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