This paper proposes a pixel-wise parameter estimation framework for Event-based Vision Sensor (EVS) characterization. Using an ordinary differential equation (ODE) based pixel latency model and an autoregressive Monte-Carlo noise model, we first identify the representative parameters of EVS. The parameter estimation is then formulated as an optimization problem to minimize the measurement-prediction error for both pixel latency and event firing probability. Finally, the effectiveness and accuracy of the proposed framework are verified by comparison of synthetic and measured event response latency as well as firing probability as function of temporal contrast (so-called S-curves).