type="main" xml:id="rssb12037-abs-0001" <title type="main">Summary</title> <p>High dimensional sparse modelling via regularization provides a powerful tool for analysing large-scale data sets and obtaining meaningful interpretable models. The use of non-convex penalty functions shows advantage in selecting important features...</p>

Variable selection plays an important role in high dimensional statistical modelling which nowadays appears in many areas and is key to various scientific discoveries. For problems of large scale or dimensionality "p", accuracy of estimation and computational cost are two top concerns. Recently,...

type="main" xml:id="rssb12023-abs-0001" <title type="main">Summary</title> <p>Model selection is of fundamental importance to high dimensional modelling featured in many contemporary applications. Classical principles of model selection include the Bayesian principle and the Kullback–Leibler divergence principle, which...</p>

We propose a new algorithm, DASSO, for fitting the entire coefficient path of the Dantzig selector with a similar computational cost to the least angle regression algorithm that is used to compute the lasso. DASSO efficiently constructs a piecewise linear path through a sequential simplex-like...

Varying-coefficient linear models arise from multivariate nonparametric regression, non-linear time series modelling and forecasting, functional data analysis, longitudinal data analysis and others. It has been a common practice to assume that the varying coefficients are functions of a given...

We propose to model multivariate volatility processes on the basis of the newly defined conditionally uncorrelated components (CUCs). This model represents a parsimonious representation for matrix-valued processes. It is flexible in the sense that each CUC may be fitted separately with any...