Multiscale Change-Point Inference

Understand

We introduce a new estimator SMUCE (simultaneous multiscale change-point estimator) for the change-point problem in exponential family regression.

• An unknown step function is estimated by minimizing the number of change-points over the acceptance region of a multiscale test at a level \alpha.
• The probability of overestimating the true number of change-points K is controlled by the asymptotic null distribution of the multiscale test statistic.
• Further, we derive exponential bounds for the probability of underestimating K.