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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
Johnson-Neyman technique: The technique which can be used in the situations where analysis of the covariance is not valid because of the heterogeneity of slopes. With this method
Hypergeometric distribution is t he probability distribution related with the sampling without replacement from the population of finite size. If the population comprises of r ele
Cauchy distribution : The probability distribution, f (x), can be given as follows where α is the position of the parameter (median) and the beta β a scale parameter. Moments
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A family of the probability distributions of the form given as here θ is the parameter and a, b, c, d are the known functions. It includes the gamma distribution, normal dis
Case-control study : The traditional case-control study is the common research design in the epidemiology where the exposures to risk factors for cases (individuals getting the dis
The variables resulting from the recoding categorical variables with more than two categories into the sequence of binary variables. Marital status, for instance, if originally lab
Computer-aided diagnosis : The computer programs which are designed to support clinical decision making. In common, such systems are based on the repeated application of the Bay
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Observation-driven model is a term generally applied to models for the longitudinal data or time series which introduce within the unit correlation by specifying the conditional
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