Posts tagged ‘BIC’

[ArXiv] classifying spectra

Variable Selection and Updating In Model-Based Discriminant Analysis for High Dimensional Data with Food Authenticity Applications
by Murphy, Dean, and Raftery

Classifying or clustering (or semi supervised learning) spectra is a very challenging problem from collecting statistical-analysis-ready data to reducing the dimensionality without sacrificing complex information in each spectrum. Not only how to estimate spiky (not differentiable) curves via statistically well defined procedures of estimating equations but also how to transform data that match the regularity conditions in statistics is challenging.
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[ArXiv] 3rd week, Jan. 2008

Seven preprints were chosen this week and two mentioned model selection. Continue reading ‘[ArXiv] 3rd week, Jan. 2008’ »

[ArXiv] 2nd week, Jan. 2007

It is notable that there’s an astronomy paper contains AIC, BIC, and Bayesian evidence in the title. The topic of the paper, unexceptionally, is cosmology like other astronomy papers discussed these (statistical) information criteria (I only found a couple of papers on model selection applied to astronomical data analysis without articulating CMB stuffs. Note that I exclude Bayes factor for the model selection purpose).

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[ArXiv] Kernel Regression, June 20, 2007

One of the papers from arxiv/astro-ph discusses kernel regression and model selection to determine photometric redshifts astro-ph/0706.2704. This paper presents their studies on choosing bandwidth of kernels via 10 fold cross-validation, choosing appropriate models from various combination of input parameters through estimating root mean square error and AIC, and evaluating their kernel regression to other regression and classification methods with root mean square errors from literature survey. They made a conclusion of flexibility in kernel regression particularly for data at high z.
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