2 citations · 4 across the 5 of their papers we have counts for
6 papers
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…
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…
-stability for cross-validation and the choice of the number of folds
Ning Xu, Jian Hong, Timothy C. G. Fisher
In this paper, we introduce a new concept of stability for cross-validation, called the -stability, and use it as a new perspective to build the general t…
Generalization error minimization: a new approach to model evaluation and selection with an application to penalized regression
Ning Xu, Jian Hong, Timothy C. G. Fisher
We study model evaluation and model selection from the perspective of generalization ability (GA): the ability of a model to predict outcomes in new samples from the same populatio…
Finite-sample and asymptotic analysis of generalization ability with an application to penalized regression
Ning Xu, Jian Hong, Timothy C. G. Fisher
In this paper, we study the performance of extremum estimators from the perspective of generalization ability (GA): the ability of a model to predict outcomes in new samples from t…
Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso
Ning Xu, Jian Hong, Timothy C. G. Fisher
Model selection is difficult to analyse yet theoretically and empirically important, especially for high-dimensional data analysis. Recently the least absolute shrinkage and select…