This article concerns the dimension reduction in regression for large data set. We introduce a new method based on the sliced inverse regression approach, called cluster-based regularized sliced inverse regression. Our method not only keeps the merit
of considering both response and predictors information, but also enhances the capability of handling highly correlated variables. It is justified under certain linearity conditions. An empirical application on a macroeconomic data set shows that our method has outperformed the dynamic factor model and other shrinkage methods.
The field-effect mobility of graphene devices is discussed. We argue that the graphene ballistic mean free path can only be extracted by taking into account both, the electrical characteristics and the channel length dependent mobility. In doing so w
e find a ballistic mean free path of 300nm at room-temperature for a carrier concentration of ~1e12/cm2 and that a substantial series resistance of around 300ohmum has to be taken into account. Furthermore, we demonstrate first quantum capacitance measurements on single-layer graphene devices.
We report the experimental observation of conductance quantization in graphene nanoribbons, where 1D transport subbands are formed due to the lateral quantum confinement. We show that this quantization in graphene nanoribbons can be observed at tempe
ratures as high as 80 K and channel lengths as long as 1.7 $mu$m. The observed quantization is in agreement with that predicted by theoretical calculations.
