Model population analysis for variable selection

To build a credible model for given chemical or biological or clinical data, it may be helpful to first get somewhat better insight into the data itself before modeling and then to present the statistically stable results derived from a large number of sub‐models established only on one dataset with...

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Bibliographic Details
Published in:Journal of chemometrics 2010-07, Vol.24 (7-8), p.418-423
Main Authors: Li, Hong-Dong, Liang, Yi-Zeng, Xu, Qing-Song, Cao, Dong-Sheng
Format: Article
Language:English
Subjects:
Assessments
biomarker discovery
Chemistry
Chemometrics
Computer simulation
Construction
Error analysis
Exact sciences and technology
General and physical chemistry
General. Nomenclature, chemical documentation, computer chemistry
Mathematical models
model population analysis
Monte Carlo methods
Monte Carlo sampling
Monte Carlo simulation
Population
Population biology
Sampling
Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry
variable selection
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Summary:To build a credible model for given chemical or biological or clinical data, it may be helpful to first get somewhat better insight into the data itself before modeling and then to present the statistically stable results derived from a large number of sub‐models established only on one dataset with the aid of Monte Carlo Sampling (MCS). In the present work, a concept model population analysis (MPA) is developed. Briefly, MPA could be considered as a general framework for developing new methods by statistically analyzing some interesting parameters (regression coefficients, prediction errors, etc.) of a number of sub‐models. New methods are expected to be developed by making full use of the interesting parameter in a novel manner. In this work, the elements of MPA are first considered and described. Then, the applications for variable selection and model assessment are emphasized with the help of MPA. Copyright © 2010 John Wiley & Sons, Ltd. We developed a concept Model Population Analysis (MPA), which could be considered as a general framework for designing new methods by statistically analyzing some interesting parameters (regression coefficients, prediction errors etc) of a large number of sub‐models. New methods are expected to be developed by making full use of the interesting parameter in a novel manner. The elements of MPA are first considered and described. Then, the applications to variable selection and model assessment are emphasized.
ISSN:0886-9383
1099-128X
1099-128X
DOI:10.1002/cem.1300