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There is a vast body of literature related to methods for detectingchangepoints (CP). However, less attention has been paid to assessing thestatistical reliability of the detected CPs. In this paper, we introduce anovel method to perform statistical inference on the significance of the CPs,estimated by a Dynamic Programming (DP)-based optimal CP detection algorithm.Based on the selective inference (SI) framework, we propose an exact(non-asymptotic) approach to compute valid p-values for testing thesignificance of the CPs. Although it is well-known that SI has low statisticalpower because of over-conditioning, we address this disadvantage by introducingparametric programming techniques. Then, we propose an efficient method toconduct SI with the minimum amount of conditioning, leading to high statisticalpower. We conduct experiments on both synthetic and real-world datasets,through which we offer evidence that our proposed method is more powerful thanexisting methods, has decent performance in terms of computational efficiency,and provides good results in many practical applications.

Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming