Least Angle Regression

In this paper we discuss the Least Angle Regression algorithm proposed by Efron et al. for variable selection. In particular, in the orthogonal case we interprete their Mallows type criterion to select the number of influential variables as a…

Basic extreme learning machines apply least square solution to calculate the neural networks output weights. In the presence of outliers and multi-collinearity, the least square solution becomes invalid. In order to fix this problem, a new kind of…

In multidimensional applications, it is very rare that all variables shift at the same time. A statistical process control procedure would have superior efficiency when limited to the subset of variables likely responsible for the out-of-control…

We utilize a variable selection method called least angle regression for finding weight and layout optimized sparse wideband arrays. As opposed to previously reported-methods for finding sparse arrays, the proposed method is attractive in that it…

In this article we consider the problem of building a linear prediction model when the number of candidate predictors is large and the data possibly contain anomalies that are difficult to visualize and clean. We want to predict the nonoutlying…

Discussion of ``Least angle regression'' by Efron et al. math.ST/0406456

Polynomial chaos (PC) expansions are used in stochastic finite element analysis to represent the random model response by a set of coefficients in a suitable (so-called polynomial chaos) basis. The number of terms to be computed grows dramatically…

Abstract 10.1002/sam.10021.abs Least angle regression and LASSO (ℓ1-penalized regression) offer a number of advantages in variable selection applications over procedures such as stepwise or ridge regression, including prediction accuracy, stability,…

Keerthi and Shevade (2007) proposed an efficient algorithm for constructing an approximate LARS solution path for logistic regression as a function of the regularization parameter. In this paper we extend their approach to multinomial regression. We…

MicroRNAs (miRNAs) are short non-coding RNAs with regulatory functions in various biological processes including cell differentiation, development and oncogenic transformation. They can bind to mRNA transcripts of protein-coding genes and repress…