  This paper considers the nonparametric regression model with negatively
super-additive dependent (NSD) noise and investigates the convergence rates of
thresholding estimators. It is shown that the term-by-term thresholding
estimator achieves nearly optimal and the block thresholding estimator attains
optimal (or nearly optimal) convergence rates over Besov spaces. Additionally,
some numerical simulations are implemented to substantiate the validity and
adaptivity of the thresholding estimators with the presence of NSD noise.
