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- W3101229196 abstract "We investigate in this paper the estimation of Gaussian graphs by model selection from a non-asymptotic point of view. We start from an n-sample of a Gaussian law ℙC in ℝp and focus on the disadvantageous case where n is smaller than p. To estimate the graph of conditional dependences of ℙC, we introduce a collection of candidate graphs and then select one of them by minimizing a penalized empirical risk. Our main result assesses the performance of the procedure in a non-asymptotic setting. We pay special attention to the maximal degree D of the graphs that we can handle, which turns to be roughly n/(2logp)." @default.
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- W3101229196 date "2008-01-01" @default.
- W3101229196 modified "2023-09-29" @default.
- W3101229196 title "Estimation of Gaussian graphs by model selection" @default.
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