Generalized additive models belong to modern techniques from statistical learning, and are applicable in many areas of prediction, e.g., in financial mathematics, computational biology, medicine, chemistry and environmental protection. These models have the form ( ) ( ) 0 1 m j j j G( ( X ) ) ψ X β f X μ = = = +∑ , where ψ is a function of the predictors. These models are fitted through local s...

High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation, and gene expression associated with a disease. An integrated genomic profiling approach measuring multiple omics data types simultaneously in the same set of biological samples would render an integrated data resolution that would not ...

Support vector machine (SVM) and other reproducing kernel Hilbert space (RKHS) based classifier systems are drawing much attention recently due to its robustness and generalization capability. All of these approaches construct classifier based on training sample in a high dimensional space by using all available dimensions. SVM achieves huge data compression by selecting only few observations l...

Microarray Technology allows us to measure the expression of thousands of genes simultaneously, and under specific conditions. Clustering is the main tool used to analyze gene expression data obtained from microarray experiments. By grouping together genes with the same behavior across samples, resultant clusters suggest new functions for some of the genes. Non-exclusive clustering algorithms a...

We study the degrees of freedom of the Lasso in the framework of Stein’s unbiased risk estimation (SURE). We show that the number of non-zero coefficients is an unbiased estimate for the degrees of freedom of the Lasso—a conclusion that requires no special assumption on the predictors. Our analysis also provides mathematical support for a related conjecture by Efron et al. (2004). As an applica...

PROSPECTIVE veterinary students can now find out more about applying for vet school and what it is like to be a qualified vet thanks to ‘Be a Vet’, a new app created by staff at the University of Glasgow’s School of Veterinary Medicine. The vet school says that many students can find the transition to life as a university veterinary student challenging, particularly if they are leaving home for...

where x ∈ R denotes a sample and xj ∈ R denotes the j-th explanatory variable for each j ∈ [1, p] , {j ∈ N|1 ≤ j ≤ p}. This was first proposed by Hastie and Tibshirani (1987) and is known as a generalized additive model (GAM). In this paper, we call fj(·) a weight function and f(·) a GAM predictor. This not only includes linear predictors but also captures nonlinear relationships between explan...

This report describes an attempt at capturing segmental transition information for speech recognition tasks. The slowly varying dynamics of spectral trajectories carries much discriminant information that is very crudely modelled by traditional approaches such as HMMs. In approaches such as recurrent neural networks there is the hope, but not the convincing demonstration, that such transitional...

This paper deals with statistical inferences based on the varying-coeecient models proposed by Hastie and Tibshirani (1993). Local polynomial regression techniques are used to estimate coeecient functions and the asymptotic normality of the resulting estimators is established. The standard error formulas for estimated coeecients are derived and are empirically tested. A goodness-of-t test techn...

This paper deals with statistical inferences based on the generalized varying-coeÆcient models proposed by Hastie and Tibshirani (1993). Local polynomial regression techniques are used to estimate coeÆcient functions and the asymptotic normality of the resulting estimators is established. The standard error formulas for estimated coeÆcients are derived and are empirically tested. A goodness-oft...