| 正文 | High-dimensional data analysis is becoming more and more important to both
academics and practitioners in finance and economics but is also very challenging
because the number of variables or parameters in connection with such data can
be larger than the sample size. Recently, several variable selection approaches have been developed and used to help us select significant variables and construct
a parsimonious model simultaneously. In this chapter, we first provide an overview
of model selection approaches in the context of penalized least squares. We
then review independence screening, a recently developed method for analyzing
ultrahigh-dimensional data where the number of variables or parameters can be
exponentially larger than the sample size. Finally, we discuss and advocate
multistage procedures that combine independence screening and variable
selection and that may be especially suitable for analyzing high-frequency
financial data. |
