Use the deterministic weights . Their squared sum and maximum are
To apply the quadratic expansion of a cumulant-generating function rigorously, write , with . Fix and let
All arguments lie in this shrinking interval. Consequently
Taking logs of the moment-generating function and using independence of random variables now gives
The case is immediate. Since is the moment-generating function of a normal distribution of variance , the convergence theorem for moment-generating functions gives
This is a weighted central limit theorem with negligible maximum weight. Its limit variance also agrees with the exact value from variance additivity for independent random variables.