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Found 3 papers, 1 papers with code
Model Selection in High-Dimensional Block-Sparse Linear Regression
In this paper, we tackle the problem of model selection in a general linear regression where the parameter matrix possesses a block-sparse structure, i. e., the non-zero entries occur in clusters or blocks and the number of such non-zero blocks is very small compared to the parameter dimension.
Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models
In this regard, extended BIC (EBIC), which is an extended version of the original BIC and extended Fisher information criterion (EFIC), which is a combination of EBIC and Fisher information criterion, are consistent estimators of the true model as the number of measurements grows very large.
Statistical model-based evaluation of neural networks
Using a statistical model-based data generation, we develop an experimental setup for the evaluation of neural networks (NNs).
