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MRIs were obtained for each subject for constraining source models to individual brain anatomy.
We addressed this initially by fitting models to individual hive data in which site was a random effect, each site comprising one cluster of six hives.
Several previous studies (Ichinose et al. 2003; Hikima and Koketsu 2005; Asano and Iwata 2009) constructed proper layered structure models to individual stations based on the waveform modeling of small events and succeeded in obtaining a detailed source rupture process.
The score was performing very well in the learning set, with a very good discrimination (AUC was 0.87 saying that predicted values for cases are all higher than for non-cases) and a very good calibration (mean goodness of fit of −0.75 and calibration curves suggesting applicability of the models to individual cases in this learning set).
However, despite the overall similarities, a number of significant differences between activation dynamics within the two models were identified, which highlights the limits of applying conclusions from generalized simple architecture models to individual cases.
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We applied logistic regression models to individual-level data to calculate adjusted odds ratios (AORs) and confidence intervals (CIs).
Consequently caution must be exercised when applying these models to individuals.
One specific technique, fitting a parametric model to individual respondent curves, has been recently proposed.
A comparison of the explosion model to individual explosive fragmentations occurring in the 1990s and observed by the Haystack and HAX radars was made.
The joint analysis follows and completes the standard procedure made in two-steps: (1) least-squares fit of a model to individual spectra; (2) evaluation of dependence of model parameters on the control variable.
We adopt a Bayesian framework for statistical inference and use MCMC methods for fitting the model to individual case data.
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