In this paper, we present a unified statistical framework for modeling both saccadic eye movements and visual saliency. By analyzing the statistical properties of human eye fixations on natural images, we found that human attention is sparsely distributed and usually deployed to locations with abundant structural information. This new observations inspired us to model saccadic behavior and visual saliency based on Super Gaussian Component (SGC) analysis. The model sequentially obtains SGC using projection pursuit, and generates eye-movements by selecting the location with maximum SGC response. Beside human saccadic behavior simulation, we also demonstrated our superior effectiveness and robustness over state-of-the-arts by carrying out dense experiments on psychological patterns and human eye fixation benchmarks. These results also show promising potentials of statistical approaches for human behavior research.