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重复测量分析中非参数变系数模型的变量选择AI 解读

Nonparametric varying-coefficient models are commonly used for analysis of data measured repeatedly over time, including longitudinal and functional responses data. While many procedures have been developed for estimating the varying-coefficients, the problem of variable selection for such models has not been addressed. In this article, we present a regularized estimation procedure for variable selection that combines basis function approximations and the smoothly clipped…

L1范数多分类支持向量机AI 解读

Binary support vector machines (SVMs) have been proven to deliver high performance. In multiclass classification, however, issues remain with respect to variable selection. One challenging issue is classification and variable selection in the presence of variables in the magnitude of thousands, greatly exceeding the size of training sample. This often occurs in genomics classification. To meet the challenge, this article proposes a novel multiclass support vector machine,…

讨论：风险最小化中的局部Rademacher复杂度与预言机不等式AI 解读

Koltchinskii is to be congratulated for developing a unified framework.This elegant framework is general and allows a user to apply it directly instead of deriving bounds in each risk minimization problem.In the past decade, the problem of risk minimization has been extensively studied in function estimation and classification.In function estimation, it has been investigated using the empirical process technique under the name of minimum contrast or sieve estimation in, for…