One issue in model-based methods is the mismatch between training and test signal levels.
Importantly, in this case there is no need to make assumptions about residual error terms linked to individual population elements, which is a core issue in model-based small-area estimation (e.g., Breidenbach and Astrup 2012, Breidenbach et al. 2015).
Convergence analysis of iterative identification-optimization schemes is a key issue in modeling for optimization of batch processes.
Another issue in modelling a natural phenomenon is that the signals might not be perfectly sparse but can only be modelled as the orthogonal sum of low-rank and sparse components.
Another issue in modelling gene regulatory systems is whether important entire elements are left out.
As ELM can be trained very quickly without iterative network weight updating, aggregating multiple ELMs does not pose any computational issues in model development.
The smoothly clipped absolute deviation (SCAD) penalty function for variable selection has nice theoretical properties, but due to its nonconvex nature, it poses computational issues in model fitting.
In this study, we highlight issues in model selection in the genome-wide association studies and problems in imputation of these special genomic regions.
Bayes rule, k-nearest neighbour method, logistic regression, score systems and artificial neural networks were examined and their performances compared, considering essential issues in model development and evaluating assumptions and adequacy.
One important issue in crowd modeling is model calibration through parameter tuning, so as to produce desired crowd behaviors.
