Dimension Reduction for Multivariate Response Data

2003 ◽  
Vol 98 (461) ◽  
pp. 99-109 ◽  
Author(s):  
Ker-Chau Li ◽  
Yve Aragon ◽  
Kerby Shedden ◽  
C Thomas Agnan
Keyword(s):  
Dimension Reduction ◽  
Response Data ◽  
Multivariate Response

scholarly journals Efficient dimension reduction for multivariate response data

2017 ◽  
Vol 155 ◽  
pp. 187-199 ◽  
Author(s):  
Yaowu Zhang ◽  
Liping Zhu ◽  
Yanyuan Ma
Keyword(s):  
Dimension Reduction ◽  
Response Data ◽  
Multivariate Response

scholarly journals Identifying and characterizing extrapolation in multivariate response data

PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0225715 ◽  
Author(s):  
Meridith L. Bartley ◽  
Ephraim M. Hanks ◽  
Erin M. Schliep ◽  
Patricia A. Soranno ◽  
Tyler Wagner
Keyword(s):  
Response Data ◽  
Multivariate Response

Multivariate Response Data

2013 ◽  
pp. 371-384
Author(s):  
Helena Geys ◽  
Christel Faes
Keyword(s):  
Response Data ◽  
Multivariate Response

Computer Simulation of Amperometric Biosensor Response to Mixtures of Compounds

2002 ◽  
Vol 7 (2) ◽  
pp. 3-14 ◽  
Author(s):  
R. Baronas ◽  
J. Christensen ◽  
F. Ivanauskas ◽  
J. Kulys
Keyword(s):  
Computer Simulation ◽  
Enzymatic Reaction ◽  
Diffusion Equations ◽  
Response Data ◽  
Finite Difference Technique ◽  
Stationary Diffusion ◽  
Biosensor Array ◽  
Menten Kinetic ◽  
Biosensor Response

A mathematical model of amperometric biosensors has been developed. The model bases on non-stationary diffusion equations containing a non-linear term related to Michaelis-Menten kinetic of the enzymatic reaction. The model describes the biosensor response to mixtures of multiple compounds in two regimes of analysis: batch and flow injection. Using computer simulation, large amount of biosensor response data were synthesised for calibration of a biosensor array to be used for characterization of wastewater. The computer simulation was carried out using the finite difference technique.

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