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In our works, a non-linear regression is done to determine the function G such that argmin G ∥ S - ( G ⊗ Y ) ∥ 2, (24).
The regression is done based on the data obtained for the state variables via Monte Carlo simulation and by choosing the trajectories where the option is in the money.
The regression is done by NN training algorithm and the NNs resulted from the training are used as the transformation function G. Thus, the NNs are the functions for mapping the reverberant feature vectors Y to the anechoic feature vector S. We use cascade NNs trained using the Cascade2 algorithm.
This yielded a total of five ranges for the JIF variable: <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0013636.e003.PNG" class= inline-graphic"/> The same regression is done separately for each JIF range by controlling all the variables (except JIF).
Note that the linear regression is done not on the frequency-ITD plots, but on the frequency-IPD plots.
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A non-linear regression was done using a monotonic function.
Regression was done at fixed CO2 composition (30%% in mass).
To control the effect of confounding variables, stepwise logistic regression was done.
Stepwise logistic regression was done to identify factors associated with disclosure.
Linear regression was done by the least squares method, and Pearson's correlation co-efficient determined.
To control for the effect of confounding variables, a stepwise logistic regression was done.
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