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This study addresses statistical inference for change point detection using the PRUTF algorithm. It introduces methods for computing p-values, constructing confidence intervals, and proposes strategies to improve their precision. Evaluation is performed on real and simulated data.

This paper introduces PRUTF, a method using trend filtering for change point detection in piecewise polynomial signals. PRUTF offers a dual solution path for efficient stopping rules and consistent pattern recovery, even in the presence of consecutive change points. Its effectiveness is demonstrated across various signals and compared with state-of-the-art methods using real-world datasets.