Results 1 to 10 of about 4,185 (251)
On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression
Mathematics, 2023 Random forest (RF) is a widely used data prediction and variable selection technique. However, the variable selection aspect of RF can become unreliable when there are more irrelevant variables than relevant ones.
Oyebayo Ridwan Olaniran +1 more
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Sparse PCA with Oracle Property. [PDF]
Adv Neural Inf Process Syst, 2014 In this paper, we study the estimation of the $k$-dimensional sparse principal subspace of covariance matrix $Σ$ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori.
Gu Q, Wang Z, Liu H.
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On Hodges’ superefficiency and merits of oracle property in model selection [PDF]
Annals of the Institute of Statistical Mathematics, 2018 29 pages, 1 ...
Xian Zhou, Xianyi Wu
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Achieving the oracle property of OEM with nonconvex penalties
Statistical Theory and Related Fields, 2017 Thepenalised least square estimator of non-convex penalties such as the smoothly clipped absolute deviation (SCAD) and the minimax concave penalty (MCP) is highly nonlinear and has many local optima.
Shifeng Xiong, Bin Dai, Peter Z. G. Qian
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On Bayesian Oracle Properties [PDF]
Bayesian Analysis, 2019 When model uncertainty is handled by Bayesian model averaging (BMA) or Bayesian model selection (BMS), the posterior distribution possesses a desirable "oracle property" for parametric inference, if for large enough data it is nearly as good as the oracle posterior, obtained by assuming unrealistically that the true model is known and only the true ...
Wenxin Jiang
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Sparse estimators and the oracle property, or the return of Hodges’ estimator [PDF]
Journal of Econometrics, 2008 18 pages, 5 ...
Benedikt M Pötscher
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Signal recovery under mutual incoherence property and oracle inequalities [PDF]
Frontiers of Mathematics in China, 2018 This paper considers signal recovery through an unconstrained minimization in the framework of mutual incoherence property. A sufficient condition is provided to guarantee the stable recovery in the noisy case. And we give a lower bound for the $\ell_2$ norm of difference of reconstructed signals and the original signal, in the sense of expectation and
Wengu Chen, Peng Li
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The Adaptive Lasso and Its Oracle Properties [PDF]
Journal of the American Statistical Association, 2006 The lasso is a popular technique for simultaneous estimation and variable selection. Lasso variable selection has been shown to be consistent under certain conditions. In this work we derive a necessary condition for the lasso variable selection to be consistent.
Hui Zou
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Surface Estimation, Variable Selection, and the Nonparametric Oracle Property. [PDF]
Stat Sin, 2011 Variable selection for multivariate nonparametric regression is an important, yet challenging, problem due, in part, to the infinite dimensionality of the function space. An ideal selection procedure should be automatic, stable, easy to use, and have desirable asymptotic properties.
Storlie CB +3 more
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The oracle property of the generalized outcome-adaptive lasso
Statistics and Probability LettersThe generalized outcome-adaptive lasso (GOAL) is a variable selection for high-dimensional causal inference proposed by Baldé et al. [2023, {\em Biometrics} {\bfseries 79(1)}, 514--520]. When the dimension is high, it is now well established that an ideal variable selection method should have the oracle property to ensure the optimal large sample ...
Ismaila Baldé
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