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Seminar on Probability and Statistics Friday November 9 2012 Tokyo 006 2:50-4:00 pm
Tuning parameter selection in sparse regression modeling
廣瀬 慧 / HIROSE, Kei 大阪大学大学院基礎工学研究科 / Graduate School of Engineering Science, Osaka University Abstract In sparse regression modeling via regularization such as
the lasso, it is important to select appropriate values of tuning
parameters including regularization parameters. The choice of tuning
parameters can be viewed as a model selection and evaluation problem.
Mallows' Cp type criteria may be used as a tuning parameter selection
tool in lasso type regularization methods, for which the concept of
degrees of freedom plays a key role. In this talk, we propose an
efficient algorithm that computes the degrees of freedom by extending
the generalized path seeking algorithm. Our procedure allows us to
construct model selection criteria for evaluating models estimated by
regularization with a wide variety of convex and nonconvex penalties.
The proposed methodology is investigated through the analysis of real
data and Monte Carlo simulations. Numerical results show that Cp
criterion based on our algorithm performs well in various situations.
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