Abstract
In many research areas today the number of features for which data is collected is much larger than the sample size based on which inference is made. This is especially true for applications in bioinformatics, but the theory presented here is of general interest in any data mining context, where the number of “interesting” features is expected to be small. In particular mBIC, mBIC1 and mBIC2 are discussed, three modifications of the Bayesian information criterion BIC which in case of an orthogonal designs control the family wise error (mBIC) and the false discovery rate (mBIC1, mBIC2), respectively. In a brief simulation study the performance of these criteria is illustrated for orthogonal and non-orthogonal regression matrices.
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Frommlet, F. (2012). Modifications of BIC for data mining under sparsity. In: Klatte, D., Lüthi, HJ., Schmedders, K. (eds) Operations Research Proceedings 2011. Operations Research Proceedings. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29210-1_39
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DOI: https://doi.org/10.1007/978-3-642-29210-1_39
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