 Simultaneous wavelet coefficient noise bound

For coefficient weights $w_{\lambda i}$ and independent mean-zero sub-Gaussian random variables of parameter $\sigma$, put $q_{\lambda }^2={\sum }_i{w}_{\lambda i}^2$. Multiplying moment-generating-function bounds shows that the weighted noise has parameter $\sigma q_{\lambda }$. The exponential Markov inequality, optimized in its parameter, gives tail bound $2\exp (-t^2/(2{\sigma }^2{q}_{\lambda }^2))$. At the displayed thresholds, a union bound over $N$ coefficients gives simultaneous control with probability at least $1-\delta$. No coefficient independence is required. Integrated wavelet weights satisfy $q_{\lambda }\le n^{-1/2}$. Finite variance alone only supports the weaker Chebyshev choice ${\tau }_{\lambda }=\sigma q_{\lambda }\sqrt{N/\delta }$.