CUSUM ($C_t=\max (0,C_{t-1}+W_t)$)

A CUSUM accumulates evidence for a change in a monitored probability distribution. With $W_t$ a log-likelihood ratio increment favoring a specified changed distribution, reset the accumulated score at zero and signal when it exceeds a chosen boundary $h$. For $C_0=0$, $C_t$ is the largest nonnegative sum of successive increments ending at $t$. This permits repeated detection of a change with an unknown onset, rather than a single fixed-start test.

For independent Poisson observations, comparing mean ${\lambda }_{0t}$ with $\theta {\lambda }_{0t}$ gives the displayed log-likelihood ratio. Updating a CUSUM with these scores targets the specified multiplicative increase. Its expected score is negative under the null and positive under the alternative when $\theta >1$, so resetting at zero discards earlier evidence against an increase.

The average run length of a CUSUM is the expectation of its first signalling time under a specified data model and initial chart state. A large in-control average run length discourages false alarms; a small out-of-control value indicates rapid detection. The mean alone does not give a finite-horizon Type I error probability or the complete detection-delay distribution.

A fast initial response CUSUM starts a CUSUM with a positive head start below its signalling boundary. Before its first reset, it accumulates a fixed-start log-likelihood ratio like a sequential probability ratio test; after resetting to zero it behaves as an ordinary CUSUM. It gives faster initial detection when the process may already be out of control, at the cost of changed initial false-alarm behavior.