We introduce a screen-space statistical filtering method for real-time rendering with global illumination. It is inspired by statistical filtering proposed by Meyer et al. to reduce the noise in global illumination over a period of time by estimating the principal components from all rendered frames. Our work extends their method to achieve nearly real-time performance on modern GPUs. More specifically, our method employs the candid covariance-free incremental PCA to overcome several limitations of the original algorithm by Meyer et al., such as its high computational cost and memory usage that hinders its implementation on GPUs. By combining the reprojection and per-pixel weighting techniques, our method handles the view changes and object movement in dynamic scenes as well.