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Neural systems with numerically matched input-output statistic: isotonic bivariate statistical modeling.

Fiori S - Comput Intell Neurosci (2007)

Bottom Line: Bivariate statistical modeling from incomplete data is a useful statistical tool that allows to discover the model underlying two data sets when the data in the two sets do not correspond in size nor in ordering.Such situation may occur when the sizes of the two data sets do not match (i.e., there are "holes" in the data) or when the data sets have been acquired independently.A number of numerical experiments, performed on both synthetic and real-world data sets, illustrate the features of the proposed modeling procedure.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Elettronica, Intelligenza Artificiale e Telecomunicazioni, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy. fiori@deit.univpm.it

ABSTRACT
Bivariate statistical modeling from incomplete data is a useful statistical tool that allows to discover the model underlying two data sets when the data in the two sets do not correspond in size nor in ordering. Such situation may occur when the sizes of the two data sets do not match (i.e., there are "holes" in the data) or when the data sets have been acquired independently. Also, statistical modeling is useful when the amount of available data is enough to show relevant statistical features of the phenomenon underlying the data. We propose to tackle the problem of statistical modeling via a neural (nonlinear) system that is able to match its input-output statistic to the statistic of the available data sets. A key point of the new implementation proposed here is that it is based on look-up-table (LUT) neural systems, which guarantee a computationally advantageous way of implementing neural systems. A number of numerical experiments, performed on both synthetic and real-world data sets, illustrate the features of the proposed modeling procedure.

No MeSH data available.


Model computed by the algorithm of Section 3.3 (Experiment on blood measurementsdata.)
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fig20: Model computed by the algorithm of Section 3.3 (Experiment on blood measurementsdata.)

Mentions: The purpose of the modeling effort proposed here isto quantify the trend of m4 measure versus patient's age, under the hypothesis that such dependency is decreasing, as suggested by analysts. The estimated cumulative distribution functions requiredby the modeling procedure are depicted in Figure 19 and the obtained model isdepicted in Figure 20. The result of modeling seems to evidence a quasilineartrend in the decrease of the m4 value versus age, in a range [22 38].


Neural systems with numerically matched input-output statistic: isotonic bivariate statistical modeling.

Fiori S - Comput Intell Neurosci (2007)

Model computed by the algorithm of Section 3.3 (Experiment on blood measurementsdata.)
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2275048&req=5

fig20: Model computed by the algorithm of Section 3.3 (Experiment on blood measurementsdata.)
Mentions: The purpose of the modeling effort proposed here isto quantify the trend of m4 measure versus patient's age, under the hypothesis that such dependency is decreasing, as suggested by analysts. The estimated cumulative distribution functions requiredby the modeling procedure are depicted in Figure 19 and the obtained model isdepicted in Figure 20. The result of modeling seems to evidence a quasilineartrend in the decrease of the m4 value versus age, in a range [22 38].

Bottom Line: Bivariate statistical modeling from incomplete data is a useful statistical tool that allows to discover the model underlying two data sets when the data in the two sets do not correspond in size nor in ordering.Such situation may occur when the sizes of the two data sets do not match (i.e., there are "holes" in the data) or when the data sets have been acquired independently.A number of numerical experiments, performed on both synthetic and real-world data sets, illustrate the features of the proposed modeling procedure.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Elettronica, Intelligenza Artificiale e Telecomunicazioni, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy. fiori@deit.univpm.it

ABSTRACT
Bivariate statistical modeling from incomplete data is a useful statistical tool that allows to discover the model underlying two data sets when the data in the two sets do not correspond in size nor in ordering. Such situation may occur when the sizes of the two data sets do not match (i.e., there are "holes" in the data) or when the data sets have been acquired independently. Also, statistical modeling is useful when the amount of available data is enough to show relevant statistical features of the phenomenon underlying the data. We propose to tackle the problem of statistical modeling via a neural (nonlinear) system that is able to match its input-output statistic to the statistic of the available data sets. A key point of the new implementation proposed here is that it is based on look-up-table (LUT) neural systems, which guarantee a computationally advantageous way of implementing neural systems. A number of numerical experiments, performed on both synthetic and real-world data sets, illustrate the features of the proposed modeling procedure.

No MeSH data available.