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5by Jason D. Lee Dennis L. Sun Yuekai Sun Jonathan E. Taylor Published in The Annals of statistics (01.06.2016)“...We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a...”
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6“...We derive the degrees of freedom of the lasso fit, placing no assumptions on the predictor matrix X. Like the well-known result of Zou, Hastie and Tibshirani...”
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7“...In many fields of science, we observe a response variable together with a large number of potential explanatory variables, and would like to be able to...”
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8by Richard Lockhart Jonathan Taylor Ryan J. Tibshirani Robert Tibshirani Published in The Annals of statistics (01.04.2014)“...In the sparse linear regression setting, we consider testing the significance of the predictor variable that enters the current lasso model, in the sequence of...”
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9by Piotr Fryzlewicz Published in The Annals of statistics (01.12.2014)“...We propose a new technique, called wild binary segmentation (WBS), for consistent estimation of the number and locations of multiple change-points in data. We...”
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10by Sara van de Geer Peter Bühlmann Ya'acov Ritov Ruben Dezeure Published in The Annals of statistics (01.06.2014)“...We propose a general method for constructing confidence intervals and statistical tests for single or low-dimensional components of a large parameter vector in...”
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11“...We present a new adaptive kernel density estimator based on linear diffusion processes. The proposed estimator builds on existing ideas for adaptive smoothing...”
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12by Cun-Hui Zhang Published in The Annals of statistics (01.04.2010)“...We propose MC+, a fast, continuous, nearly unbiased and accurate method of penalized variable selection in high-dimensional linear regression. The LASSO is...”
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13“...Random forests are a learning algorithm proposed by Breiman [Mach. Learn. 45 (2001) 5-32] that combines several randomized decision trees and aggregates their...”
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14“...This paper considers the noisy sparse phase retrieval problem: recovering a sparse signal x ϵ ℝp from noisy quadratic measurements yj = (a′jx)² + εj, j = 1 , ...”
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15“...Networks or graphs can easily represent a diverse set of data sources that are characterized by interacting units or actors. Social networks, representing...”
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16by Ismaël Castillo Johannes Schmidt-Hieber Aad van der Vaart Published in The Annals of statistics (01.10.2015)“...We study full Bayesian procedures for high-dimensional linear regression under sparsity constraints. The prior is a mixture of point masses at zero and...”
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17“...We propose generalized random forests, a method for nonparametric statistical estimation based on random forests (Breiman [Mach. Learn. 45 (2001) 5–32]) that...”
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18“...In the period 1991-2015, algorithmic advances in Mixed Integer Optimization (MIO) coupled with hardware improvements have resulted in an astonishing 450...”
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19by Peter J. Bickel Ya'acov Ritov Alexandre B. Tsybakov Published in The Annals of statistics (01.08.2009)“...We show that, under a sparsity scenario, the Lasso estimator and the Dantzig selector exhibit similar behavior. For both methods, we derive, in parallel,...”
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20by Pradeep Ravikumar Martin J. Wainwright John D. Lafferty Published in The Annals of statistics (01.06.2010)“...We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on ℓ₁-regularized logistic...”