We develop new confidence-interval estimators for the mean and variance parameter of a steady-state simulation output process. These confidence intervals are based on optimal linear combinations of overlapping estimators for the variance parameter. We present analytical and simulation-based results exemplifying the potential of this technique for improvements in accuracy for confidence intervals.