2 citations · 3 across the 5 of their papers we have counts for
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stat.ML2020★ 2 cited
Rademacher upper bounds for cross-validation errors with an application to the lasso
Ning Xu, Timothy C. G. Fisher, Jian Hong
We establish a general upper bound for -fold cross-validation (-CV) errors that can be adapted to many -CV-based estimators and learning algorithms. Based on Rademacher co…
stat.ML2020
Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing
Ning Xu, Timothy C. G. Fisher, Jian Hong
Endogeneity bias and instrument variable validation have always been important topics in statistics and econometrics. In the era of big data, such issues typically combine with dim…
stat.ML2020
Accuracy and stability of solar variable selection comparison under complicated dependence structures
Ning Xu, Timothy C. G. Fisher, Jian Hong
In this paper we focus on the empirical variable-selection peformance of subsample-ordered least angle regression (Solar) -- a novel ultrahigh dimensional redesign of lasso -- on t…