Model Selection for High-Dimensional Problems
Jing-Zhi Huang, Zhan Shi, Wei Zhong
Handbook of Financial Econometrics and Statistics
#002304 20160221 ()
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.
Keywords: Model selection • Variable selection • Dimension reduction • Independence screening • High-dimensional data • Ultrahigh-dimensional data • Generalized correlations • Penalized least squares • Shrinkag

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