Abstract: In order to study the impact of censored demand information on retailers' optimal decision-making, a data-driven distributionally robust newsboy pricing and inventory joint decision model is established with the objective of minimizing the total cost, including inventory holding cost and stockout penalty cost. Firstly, the censored demand information is introduced into the distribution uncertainty set, and Cox regression is used to estimate the censored value to determine the parameters of the demand uncertainty set. The original problem is then reformulated into a dual problem and solved by the Rsome analysis tool. Subsequently, the efficiency of the distributionally robust optimization method proposed in this paper is evaluated at different parameter levels through random numerical experiments. Results show that compared with the common optimization algorithms (i.e., robust optimization and stochastic optimization), the proposed method reduces the average cost of 23.74% and 16.32%, respectively. Moreover, the average cost difference between the proposed method and the theoretical optimal case is narrowed to 21.41%. Finally, the effectiveness of the distributionally robust optimization method proposed is further verified based on real case data.