Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Fisher, Timothy C. G."'
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 dimensionality issues and, hence, require even more attention. In this pa
Externí odkaz:
http://arxiv.org/abs/2007.15769
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 the empirical data with complicated dependence structures and, hence, s
Externí odkaz:
http://arxiv.org/abs/2007.15614
We establish a general upper bound for $K$-fold cross-validation ($K$-CV) errors that can be adapted to many $K$-CV-based estimators and learning algorithms. Based on Rademacher complexity of the model and the Orlicz-$\Psi_{\nu}$ norm of the error pr
Externí odkaz:
http://arxiv.org/abs/2007.15598
Autor:
Xu, Ning, Fisher, Timothy C. G.
We propose a new variable selection algorithm, subsample-ordered least-angle regression (solar), and its coordinate descent generalization, solar-cd. Solar re-constructs lasso paths using the $L_0$ norm and averages the resulting solution paths acros
Externí odkaz:
http://arxiv.org/abs/2007.15707
In this paper, we introduce a new concept of stability for cross-validation, called the $\left( \beta, \varpi \right)$-stability, and use it as a new perspective to build the general theory for cross-validation. The $\left( \beta, \varpi \right)$-sta
Externí odkaz:
http://arxiv.org/abs/1705.07349
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 population. We believe that GA is one way formally to address concerns about th
Externí odkaz:
http://arxiv.org/abs/1610.05448
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 the same population. By adapting the classical concentration inequaliti
Externí odkaz:
http://arxiv.org/abs/1609.03344
Model selection is difficult to analyse yet theoretically and empirically important, especially for high-dimensional data analysis. Recently the least absolute shrinkage and selection operator (Lasso) has been applied in the statistical and econometr
Externí odkaz:
http://arxiv.org/abs/1606.00142