Microarray data profiles gene expression on a whole genome scale, and provides a good way to study associations between gene expression and occurrence or progression of cancer disease. Many researchers realized that microarray data is useful to predict cancer cases. However, the high dimension of gene expressions, which is significantly larger than the sample size, makes this task very difficul...

Genome-scale gene networks contain regulatory genes called hubs that have many interaction partners. These genes usually play an essential role in gene regulation and cellular processes. Despite recent advancements in high-throughput technology, inferring gene networks with hub genes from highdimensional data still remains a challenging problem. Novel statistical network inference methods are n...

OBJECTIVE Glucolipotoxicity is a major pathophysiological mechanism in the development of insulin resistance and type 2 diabetes mellitus (T2D). We aimed to detect subtle changes in the circulating lipid profile by shotgun lipidomics analyses and to associate them with four different insulin sensitivity indices. METHODS The cross-sectional study comprised 90 men with a broad range of insulin ...

Eigenphone-based speaker adaptation outperforms conventional maximum likelihood linear regression (MLLR) and eigenvoice methods when there is sufficient adaptation data. However, it suffers from severe over-fitting when only a few seconds of adaptation data are provided. In this paper, various regularization methods are investigated to obtain a more robust speaker-dependent eigenphone matrix es...

PURPOSE To improve the neurological hemifield test (NHT) using visual field data from both eyes to detect and classify visual field loss caused by chiasmal or postchiasmal lesions. METHODS Visual field and clinical data for 633 patients were divided into a training set (474 cases) and a validation set (159 cases). Each set had equal numbers of neurological, glaucoma, or glaucoma suspect cases...

Abstract: The problem of estimating the centre of symmetry of an unknown periodic function observed in Gaussian white noise is considered. Using the penalized blockwise James-Stein method, a smoothing filter allowing to define the penalized profile likelihood is proposed. The estimator of the centre of symmetry is then the maximizer of this penalized profile likelihood. This estimator is shown ...

Hierarchical models are powerful statistical tools widely used in scientific and engineering applications. The homoscedastic (equal variance) case has been extensively studied, and it is well known that shrinkage estimates, the James-Stein estimate in particular, offer nice theoretical (e.g., risk) properties. The heteroscedastic (the unequal variance) case, on the other hand, has received less...

Shrinkage estimation usually reduces variance at the cost of bias. But when we care only about some parameters of a model, I show that we can reduce variance without incurring bias if we have additional information about the distribution of covariates. In a linear regression model with homoscedastic Normal noise, I consider shrinkage estimation of the nuisance parameters associated with control...

Thermal errors significantly affect the accurate performance of computer numerical control (CNC) machine tools. In this paper, an improved robust thermal error prediction approach is proposed for CNC tools based on adaptive Least Absolute Shrinkage and Selection Operator (LASSO) eXtreme Gradient Boosting (XGBoost) algorithms. Specifically, LASSO method enjoys oracle property selecting temperatu...

Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry. This paper tackles the problem of sparse coding and dictionary learning in the space of symmetric positive definite matrices, which form a Riemannian manifold. With the aid of the recently introduced Stein kernel (related to a symmetric version of Breg...