Tests of Hypotheses in a Set of Spectral Courses Alias Marker Statistics
We describe a universal basic regression algorithm for the identification of marker peak areas in spectral sets. Tests in a set of orthogonal polynomial regressions is the basic principle of this approach. Gnostic cluster analysis is then a very effective algorithmic complement, applicable in the case of excessive behavior of some markers. This new regression algorithm aims at recognition of quantitative dependences of (bio)markers in
general. A special emphasis is put on application in mass spectrometry methods, which has made a great progress in the last 10 years.
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