Summary: Graphical models are frequently used to explore networks, such as genetic networks, among a set of variables. This is usually carried out via exploring the sparsity of the precision matrix of the variables under consideration. Penalized likelihood methods are often used in such explorations. Yet, positive-definiteness constraints of precision matrices make the optimization problem challenging.
We introduce nonconcave penalties and the adaptive LASSO penalty to attenuate the bias problem in the network estimation. Through the local linear approximation to the nonconcave penalty functions, the problem of precision matrix estimation is recast as a sequence of penalized likelihood problems with a weighted $$L_{1}$$ penalty and solved using the efficient algorithm of J. Friedman et al. [Biostatistics 9, 432–441 (2008; Zbl 1143.62076)]. Our estimation schemes are applied to two real datasets. Simulation experiments and asymptotic theory are used to justify our proposed methods.

MSC:

 62H12 Estimation in multivariate analysis 65C05 Monte Carlo methods 65C60 Computational problems in statistics (MSC2010)

Zbl 1143.62076

Software:

HdBCS; glasso; MIM
Full Text:

References:

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