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An efficient method for smoothing indicator-dilution and other unimodal curves
J B Bassingthwaighte1, I S Chan, A A Goldstein
1Center for Bioengineering, University of Washington, Seattle 98195.
Summary
A new mathematical function effectively smooths noisy, non-Gaussian data, offering a computationally efficient alternative to cubic splines for applications like indicator-dilution curves.
Area of Science:
- Mathematical Modeling
- Data Analysis
Background:
- Experimental data often contains noise, requiring smoothing techniques.
- Existing methods like cubic splines can be computationally intensive and require significant storage.
Purpose of the Study:
- To develop a novel mathematical function for approximating unimodal functions.
- To provide an efficient alternative to cubic splines for smoothing noisy data, especially non-Gaussian, skewed, or incomplete datasets.
Main Methods:
- Development of a new mathematical function for unimodal function approximation.
- Implementation of the function in a Fortran routine named SMOEX.
- Comparison of SMOEX with standard cubic spline smoothing routines.
Main Results:
- The new function accurately approximates various unimodal functions, including non-Gaussian and skewed forms.
- SMOEX demonstrates computational inexpensiveness compared to cubic splines.
- SMOEX requires less storage for preserving smoothed functions.
Conclusions:
- The developed mathematical function and its SMOEX implementation offer an efficient and less storage-intensive method for smoothing noisy experimental data.
- This approach is particularly beneficial for applications involving indicator-dilution curves and probability density functions.