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%2C a change occurs at some unknown time in the distribution of a sequence of independent observations. This work studies a QCD problem where the change is either a bad change%2C which we aim to detect%2C or a confusing change%2C which is not of our interest. Our objective is to detect a bad change as quickly as possible while avoiding raising a false alarm for pre-change or a confusing change. We identify a specific set of pre-change%2C bad change%2C and confusing change distributions that pose challenges beyond the capabilities of standard Cumulative Sum (CuSum) procedures. Proposing novel CuSum-based detection procedures%2C S-CuSum and J-CuSum%2C leveraging two CuSum statistics%2C we offer solutions applicable across all kinds of pre-change%2C bad change%2C and confusing change distributions. For both S-CuSum and J-CuSum%2C we provide analytical performance guarantees and validate them by numerical results. Furthermore%2C both procedures are computationally efficient as they only require simple recursive updates.?quality=80&w=800)
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