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Robust inference with knockoffs

WebDec 12, 2024 · ROBUST INFERENCE. \it Elvezio Ronchetti [1] Professor, Department of Econometrics. University of Geneva, CH-1211 Geneva, Switzerland. The primary goal of robust statistics is the development of procedures which are still reliable and reasonably efficient under small deviations from the model, i.e. when the underlying distribution lies … WebJul 17, 2024 · The knockoff procedure is a recent breakthrough in statistics that, in theory, can identify truly correlated features while guaranteeing that the false discovery is limited. The idea is to create synthetic data -knockoffs- that …

DeepLINK: Deep learning inference using knockoffs with ... - PNAS

WebFeb 10, 2016 · This paper develops an exact and efficient algorithm to sample knockoff copies of an HMM, and argues that combined with the knockoffs selective framework, they provide a natural and powerful tool for performing principled inference in genome-wide association studies with guaranteed FDR control. 39 PDF View 3 excerpts, cites methods … Webknockoff filter scheme, called Error-based Knockoffs Infer-ence (E-Knockoff), for controlled feature selection based on the error-based feature statistics. The main contributions of this paper are summarized as below: Error-based knockoffs inference. Our model integrates the knockoff features (Candes et al. 2024), the error-` indy clutch and joint https://illuminateyourlife.org

Robust inference with knockoffs - ResearchGate

WebWe develop a method for deep learning inference using knockoffs, DeepLINK, to achieve the goal of variable selection with controlled error rate in deep learning models. We show that DeepLINK can also have high power in variable selection with a … WebA knockoff filter for high-dimensional selective inference. RF Barber, EJ Candès. arXiv preprint arXiv:1602.03574, 2016. 152: ... Robust inference with knockoffs. RF Barber, EJ Candès, RJ Samworth. 96: 2024: Global identifiability of linear structural equation models. M Drton, R Foygel, S Sullivant. 93: WebNov 12, 2024 · Bayesian Knockoff Generators for Robust Inference Under Complex Data Structure 11/12/2024 ∙ by Michael J. Martens, et al. ∙ 0 ∙ share The recent proliferation of medical data, such as genetics and electronic health records (EHR), offers new opportunities to find novel predictors of health outcomes. indy clowns of the negro leagues

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Robust inference with knockoffs

RANK: Large-Scale Inference with Graphical Nonlinear Knockoffs

WebThe model-X knockoff framework is therefore robust to errors in the underlying assumptions on the distribution of $X$, making it an effective method for many practical applications, … WebWe consider the variable selection problem, which seeks to identify important variables influencing a response $Y$ out of many candidate fea

Robust inference with knockoffs

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WebJan 11, 2024 · This paper develops an exact and efficient algorithm to sample knockoff copies of an HMM, and argues that combined with the knockoffs selective framework, …

WebThe knockoff filter is introduced, a new variable selection procedure controlling the FDR in the statistical linear model whenever there are at least as many observations as variables, … WebBayesian Knockoff Generators for Robust Inference Under Complex Data StructureA PREPRINT the sample size (p n). A subsequent development (Candes et al., 2024) generates knockoffs randomly...

http://www.statslab.cam.ac.uk/~rjs57/euclid.aos.1594972823.pdf WebThis model selection procedure operates by constructing “knockoff copies” of each of the p p features, which are then used as a control group to ensure that the model selection …

WebRobust inference with the knockoff filter. In this talk, I will present ongoing work on the knockoff filter for inference in regression. In a high-dimensional model selection problem, we would like to select relevant features without too many false positives. The knockoff filter provides a tool for model selection by creating knockoff copies of ...

WebSequoia Hall 390 Jane Stanford Way Stanford, CA 94305-4020 Campus Map login hera commWebMay 22, 2024 · Knockoff (KO) inference is intractable in high-dimensional settings, as knockoff generation requires the estimation and inversion of covariance matrices of size … login heriot wattWebWe consider the variable selection problem, which seeks to identify important variables influencing a response $Y$ out of many candidate features $X_1, \ldots, X_p ... indycoWebWe introduce a method to draw causal inferences-inferences immune to all possible confounding-from genetic data that include parents and offspring. Causal conclusions are possible with these data because the natural … login herincoWebNov 12, 2024 · Knockoff filtering is an innovative strategy for FDR-controlled feature selection. But, existing knockoff methods make strong distributional assumptions that … indy coatingWebknockoff filter scheme, called Error-based Knockoffs Infer-ence (E-Knockoff), for controlled feature selection based on the error-based feature statistics. The main contributions of this paper are summarized as below: • Error-based knockoffs inference. Our model integrates the knockoff features (Candes et al. 2024), the error-` indy coachWebNov 12, 2024 · Bayesian Knockoff Generators for Robust Inference Under Complex Data Structure. The recent proliferation of medical data, such as genetics and electronic health … login heritagegas.com