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SUMMARY We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian graphical model. The methods lead to a sparse and shrinkage estimator of the concentration matrix that is positive definite, and thus conduct model selection and estimation simultaneously. The implementation of the methods is nontrivial because of the(More)
1 This is a revision of TR1064, September, 2002. Some initial expository material has been removed at the request of a referee. There are some new results presented concerning the relations between the Bayes rule and other proposed SVM methods for the multicategory case. See especially the paragraph containing Eq (9) on p 8. Abstract Two category Support(More)
We propose a new method for model selection and model fitting in nonparametric regression models, in the framework of smoothing spline ANOVA. The " COSSO " is a method of regularization with the penalty functional being the sum of component norms, instead of the squared norm employed in the traditional smoothing spline method. The COSSO provides a unified(More)
The Support Vector Machine (SVM) has shown great performance in practice as a classification methodology. Oftentimes multicategory problems have been treated as a series of binary problems in the SVM paradigm. Even though the SVM implements the optimal classification rule asymptotically in the binary case, solutions to a series of binary problems may not be(More)
We study the non-negative garrotte estimator from three different aspects: consistency , computation and flexibility. We argue that the non-negative garrotte is a general procedure that can be used in combination with estimators other than the original least squares estimator as in its original form. In particular, we consider using the lasso, the elastic(More)
Many cluster based replication solutions have been proposed providing scalability and fault-tolerance. Many of these solutions perform replica control in a middleware on top of the database replicas. In such a setting concurrency control is a challenge and is often performed on a table basis. Additionally, some systems put severe requirements on transaction(More)
The majority of classification algorithms are developed for the standard situation in which it is assumed that the examples in the training set come from the same distribution as that of the target population, and that the cost of misclassification into different classes are the same. However, these assumptions are often violated in real world settings. For(More)