The long-term degradation due to aging such as NBTI (Negative Bias Temperature Instability) is a hot issue in the current circuit design using nanometer process technologies, since it causes a delay fault in the field. In order to resolve the problem, we must estimate delay variation caused by long-term degradation in design stage, but over estimation must be avoided so as to make timing design easier. If we can treat such a variation statistically, and if we treat it together with delay variations due to process variability, then we can reduce over margin in timing design. Moreover, such a statistical static timing analyzer treating process variability and long-term degradation together help us to select an appropriate set of paths for which field testing are conducted to detect delay faults. In this paper, we propose a new delay model taking long-term degradation into account for statistical static timing analysis, and propose an algorithm for finding the statistical maximum, which is one of key operations in statistical static timing analysis. We also show a few experimental results demonstrating the effect of the algorithm.