Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie [H. Zou, T. Hastie, Regularization and variable selection via the elastic net, J. R. Stat. Soc. Ser. B, 67(2) (2005) 301–320] for the selection of groups of correlated variables. To investigate the statistical properties of this scheme and in partic...

We are very grateful to the Executive Editors George Casella and Edward George for their active interest in our paper and for organizing this challenging discussion. We also thank all the discussants for their insightful and stimulating comments. When we submitted the original manuscript in 2003, we were tempted to go for a more general paper on kernel methods. We decided to focus on support ve...

In this article I propose that categorization decisions are often made relative to causal models of categories that people possess. According to this causal-model theory o f categorization, evidence of an exemplar's membership in a category consists of the likelihood that such an exemplar can be generated by the category's causal model. Bayesian networks are proposed as a representation of thes...

This study examined whether false memories, as revealed by the Deese-Roediger-McDermott (DRM) paradigm, can arise from indirect stereotype associations, as proposed by Lenton, Blair, and Hastie (2001). We found significant indications of stereotype-evoked false memories. The participants in our experiment reported that they were unaware of the gender theme of the studied list, suggesting that t...

We propose goodness of t tests for testing generalized linear models and semiparametric regression models against smooth alternatives The focus is on models having both continuous and factorial covariates As smooth extension of a parametric or semiparametric model we use generalized varying coe cient models as proposed by Hastie Tibshirani A likelihood ratio statistic is used for testing and as...

Recent research (e.g., Hutter, Crisp, Humphreys, Waters, & Moffit; Siebler) has confirmed that combining novel social categories involves two stages (e.g., Hampton; Hastie, Schroeder, & Weber). Furthermore, it is also evident that following stage 1 (constituent additivity), the second stage in these models involves cognitively effortful complex reasoning. However, while current theory and resea...

Abstract: We consider the least angle regression and forward stagewise algorithms for solving penalized least squares regression problems. In Efron, Hastie, Johnstone & Tibshirani (2004) it is proved that the least angle regression algorithm, with a small modification, solves the lasso regression problem. Here we give an analogous result for incremental forward stagewise regression, showing tha...

Several applications of nonlinear principal component analysis (NPCA) have appeared recently in process monitoring and fault diagnosis. In this paper a new approach is proposed for fault detection based on principal curves and genetic algorithms. The principal curve is a generation of linear principal component (PCA) introduced by Hastie as a parametric curve passes satisfactorily through the m...

The present paper tests the widely accepted hypothesis that on-line judgment implies functional independence between memory for, and judgment of, verbal stimuli (e.g., Anderson, 1989; Hastie & Park, 1986). In the present study, participants recalled lists of words, after having assessed each for its pleasantness. Presentation position of a negative item within the lists was manipulated. Also, i...

In this study, we consider several regularization path algorithms with grouped variable selection for modeling gene-interactions. When fitting with categorical factors, including the genotype measurements, we often define a set of dummy variables that represent a single factor/interaction of factors. Yuan & Lin (2006) proposed the groupLars and the group-Lasso methods through which these groups...