Model selection for estimating the non zero components of a gaussian vector
ESAIM: Probability and Statistics, Tome 10 (2006), pp. 164-183

We propose a method based on a penalised likelihood criterion, for estimating the number on non-zero components of the mean of a gaussian vector. Following the work of Birgé and Massart in gaussian model selection, we choose the penalty function such that the resulting estimator minimises the Kullback risk.

DOI : 10.1051/ps:2006004
Classification : 62G05, 62G09
Keywords: Kullback risk, model selection, penalised likelihood criteria 
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     author = {Huet, Sylvie},
     title = {Model selection for estimating the non zero components of a gaussian vector},
     journal = {ESAIM: Probability and Statistics},
     pages = {164--183},
     year = {2006},
     publisher = {EDP Sciences},
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     doi = {10.1051/ps:2006004},
     mrnumber = {2218407},
     language = {en},
     url = {https://www.numdam.org/articles/10.1051/ps:2006004/}
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Huet, Sylvie. Model selection for estimating the non zero components of a gaussian vector. ESAIM: Probability and Statistics, Tome 10 (2006), pp. 164-183. doi: 10.1051/ps:2006004

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