Statistical models with diverging dimensionality

Nowadays in many statistical applications, we face models whose complexity increases with the sample size. Such models pose a challenge to the traditional statistical analysis, and call for new methodologies and new asymptotic studies, which are exactly the focus of my thesis. In particular, my thesis consists of three parts: i) a novel non-parametric qualification procedure for lysate protein microarray; ii) theoretic analysis for one-way ANOVA with diverging dimensionality and iii) statistical analysis for multi-task learning