This paper is a sequel to our previous exploration (Chen et al., 2014) of the multivariate modeling (MVM) approach for FMRI group analysis. The layout is as follows. First, we explore and review various hypothesis testing strategies at the group level when the HDR is estimated through multiple basis functions. Second, simulation data were generated to reveal how each methodology performs in terms of controllability for false positives and false negatives, and the performance of these methods was assessed when they were applied to the experimental dataset at both individual and group levels. Finally, we compare all the modeling methodologies for ASM and ESM as well as with and without dimension reduction. The modeling strategies and testing methods discussed here are all performed at the voxel level. Multiple testing correction can be applied in the conventional fashion by controlling the false positive rate (Benjamini and Hochberg, 1995) or the family-wise error through Monte Carlo simulations (3dClustSim in AFNI, Forman et al., 1995) or random field theory (Worsley et al., 1992).