SeminarsData-driven Selection of Penalty in Lasso
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Due to rapid development in large dimensional data acquisitions such as microarray, scientists look for useful methods on predicting a quantitative measurement when number of predictors p is much greater than n, the sample size. In this talk, we will address issues arose from the data-driven selection of penalty in Lasso when n is of the same order p. In particular, we discuss orthogonal predictors and Mallows’ Cp. BIC will also be addressed.
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Hung Chen
2008-09-26
15:30:00 - 17:00:00
301 , Freshman Classroom Building
Due to rapid development in large dimensional data acquisitions such as microarray, scientists look for useful methods on predicting a quantitative measurement when number of predictors p is much greater than n, the sample size. In this talk, we will address issues arose from the data-driven selection of penalty in Lasso when n is of the same order p. In particular, we discuss orthogonal predictors and Mallows’ Cp. BIC will also be addressed.