Abstract Classification of real-world data poses a number of challenging problems. Mismatch between classifier models and true data distributions on one hand and the use of approximate inference methods on the other hand all contribute to inaccurate classification. Recent work on boosting by Schapire et al. and additive probabilistic models by Hastie et al. have shown that improved classificati...

We propose a new statistics for the detection of differentially expressed genes when the genes are activated only in a subset of the samples. Statistics designed for this unconventional circumstance has proved to be valuable for most cancer studies, where oncogenes are activated for a small number of disease samples. Previous efforts made in this direction include cancer outlier profile analysi...

Several classes of computational and statistical methods for data mining are available. Each class can be parameterised so that models within the class differ in terms of such parameters (see, for instance, Giudici, 2003; Hastie et al., 2001; Han & Kamber, 2000; Hand et al., 2001; Witten & Frank, 1999): for example, the class of linear regression models, which differ in the number of explanator...

In recent years there has been substantial interest in high-dimensional estimation and prediction problems on large graphs. These can in many cases be seen as high-dimensional or nonparametric regression or classification problems in which the goal is to learn a “smooth” function on a given graph. Various methods have been proposed to deal with such problems, motivated by a variety of applicati...

Boosting, or boosted regression, is a recent data mining technique that has shown considerable success in predictive accuracy. This article gives an overview over boosting and introduces a new Stata command, boost, that implements the boosting algorithm described in Hastie et al. (2001, p. 322). The plugin is illustrated with a Gaussian and a logistic regression example. In the Gaussian regress...

We consider the problems of estimating the parameters as well as the structure of binary-valued Markov networks. For maximizing the penalized log-likelihood, we implement an approximate procedure based on the pseudo-likelihood of Besag (1975) and generalize it to a fast exact algorithm. The exact algorithm starts with the pseudo-likelihood solution and then adjusts the pseudo-likelihood criteri...

Background: The insulin-like growth factor binding protein-3 (IGFBP-3) gene is a structural gene responsible for the multiple effects of insulin-like growth factors (IGFs) playing a key role in mammalian growth, development and reproduction (BALE et al., 1992; HASTIE et al., 2004). Single nucleotide polymorphisms (SNPs) have been described in the bovine IGFBP3 gene which was associated with pro...

The additive model is one of the most popular semiparametric models. The backfitting estimation (Buja, Hastie and Tibshirani, 1989, Ann. Statist.) for the model is intuitively easy to understand and theoretically most efficient (Opsomer and Ruppert, 1997, Ann. Statist.); its implementation is equivalent to solving simple linear equations. However, convergence of the algorithm is very difficult ...

For least squares regression, Efron et al. (2004) proposed an efficient solution path algorithm, the least angle regression (LAR). They showed that a slight modification of the LAR leads to the whole LASSO solution path. Both the LAR and LASSO solution paths are piecewise linear. Recently Wu (2011) extended the LAR to generalized linear models and the quasi-likelihood method. In this work we ex...