Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized lin...

In this paper, we study the generalization ability (GA)—the ability of a model to predict outcomes in new samples from the same population—of the extremum estimators. By adapting the classical concentration inequalities, we propose upper bounds for the empirical out-ofsample prediction error for extremum estimators, which is a function of the in-sample error, the severity of heavy tails, the sa...

Every known link between a genetic variant and blood pressure improves the understanding and potentially the risk assessment of related diseases such as hypertension. Genetic data have become increasingly comprehensive and available for an increasing number of samples. The availability of whole-genome sequencing data means that statistical genetic models must evolve to meet the challenge of usi...

Abstract. This paper proposes a penalized quantile regression estimator for panel data that explicitly considers individual heterogeneity associated with the covariates. We provide conditions under which the estimator is asymptotically Gaussian, and the harshness of the penalization can be determined by minimizing asymptotic mean squared error. We investigate finite sample and asymptotic perfor...

I discuss the production of low rank smoothers for d ≥ 1 dimensional data, which can be fitted by regression or penalized regression methods. The smoothers are constructed by a simple transformation and truncation of the basis that arises from the solution of the thinplate spline smoothing problem, and are optimal in the sense that the truncation is designed to result in the minimum possible pe...