State of the art statistical timing analysis (STA) tools often yield less accurate results when timing variables become correlated due to global source of variations and path reconvergence. To the best of our knowledge, no good solution is available dealing both types of correlations simultaneously. In this paper, we present a novel extended pseudo-canonical timing model to retain and evaluate both type of correlation during statistical timing analysis with minimum computation cost. Also, an intelligent pruning method is introduced to enable trade-off runtime with accuracy. Tested with ISCAS benchmark suites, our method shows both high accuracy and high performance. For example, on the circuit c6288, our distribution estimation error shows 15? accuracy improvement compared with previous approaches.