【摘 要】
:
In the linear model,Fan and Lv (2008) showed that simple correlation ranking possesses a sure independence screening property under certain conditions and t
【机 构】
:
University of Cambridge
【出 处】
:
2009 International Conference on Financial Statistics and Fi
论文部分内容阅读
In the linear model,Fan and Lv (2008) showed that simple correlation ranking possesses a sure independence screening property under certain conditions and that its revision,called iterated sure independence screening (ISIS),may be more useful when features are highly correlated.We extend ISIS,without explicit definition of residuals,to a general pseudo-likelihood framework,which includes generalized linear models as a special case.Even in the least-squares setting,the new method improves ISIS by allowing variable deletion in the iterative process,and we can also select important features in high-dimensional classification where the frequently-used two-sample $t$-method may fail.
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