2 citations · 2 across the 1 of their papers we have counts for
7 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…
Solar: solution path averaging for fast and accurate variable selection in high-dimensional data
Ning Xu, Timothy C. G. Fisher
We propose a new variable selection algorithm, subsample-ordered least-angle regression (solar), and its coordinate descent generalization, solar-cd. Solar re-constructs lasso path…
-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…