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Application Fields of the Empirical Regression ‐ A Case Study
Author(s) -
Peil J.,
Schmerling S.
Publication year - 1985
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710270306
Subject(s) - series (stratigraphy) , smoothing , mathematics , interpolation (computer graphics) , regression analysis , regression , basis (linear algebra) , statistics , linear regression , time series , relation (database) , component (thermodynamics) , econometrics , computer science , data mining , artificial intelligence , biology , thermodynamics , motion (physics) , paleontology , physics , geometry
Empirical regression is defined as conditional expected value based on an estimation of a twodimensional density. It is a modelfree mathematical means for a first evaluation of measured data of an unknown stochastical relation between two quantities. The numerical procedures may be applied for calculation of the mean course of an unknown relation hidden in the measured data. Disregarding the statistical background an other aspect of application is the analyzing of time series, especially smoothing of time series and modelfree recording of the trend component in non‐stationary time series. The calculated regression curve provides an objective basis for comparing of different measured courses as well as for a further evaluation, e. g. in respect of a suitable choice of an analytical expression. The possibility of interpolation and the smoothing properties of empirical regression give essential advantages for internal regression as one step in the process of model construction.