Results 11 to 20 of about 396 (97)
Smoothing ℓ1-penalized estimators for high-dimensional time-course data [PDF]
, 2007 When a series of (related) linear models has to be estimated it is often appropriate to combine the different data-sets to construct more efficient estimators. We usel1-penalized estimators like the Lasso or the Adaptive Lasso which can simultaneously do
L. Meier, Peter Buhlmann
semanticscholar +1 more source
Pac-bayesian bounds for sparse regression estimation with exponential weights [PDF]
, 2010 We consider the sparse regression model where the number of parameters $p$ is larger than the sample size $n$. The difficulty when considering high-dimensional problems is to propose estimators achieving a good compromise between statistical and ...
Alquier, Pierre, Lounici, Karim
core +4 more sources
, 2014 The conditional Akaike information criterion, AIC, has been frequently used for model selection in linear mixed models. We develop a general framework for the calculation of the conditional AIC for different exponential family distributions. This unified
Benjamin Saefken +3 more
semanticscholar +1 more source
Sparse Conformal Predictors [PDF]
, 2009 Conformal predictors, introduced by Vovk et al. (2005), serve to build prediction intervals by exploiting a notion of conformity of the new data point with previously observed data.
Hebiri, Mohamed
core +5 more sources
Ridge regression for the functional concurrent model
, 2018 The aim of this paper is to propose estimators of the unknown functional coefficients in the Functional Concurrent Model (FCM). We extend the Ridge Regression method developed in the classical linear case to the functional data framework.
Tito Manrique +2 more
semanticscholar +1 more source
On the adaptive elastic-net with a diverging number of parameters [PDF]
, 2009 We consider the problem of model selection and estimation in situations where the number of parameters diverges with the sample size. When the dimension is high, an ideal method should have the oracle property [J. Amer. Statist. Assoc.
Zhang, Hao Helen, Zou, Hui
core +2 more sources
Nonconcave penalized estimation in sparse vector autoregression model
Electronic Journal of Statistics, 2020 High dimensional time series receive considerable attention recently, whose temporal and cross-sectional dependency could be captured by the vector autoregression (VAR) model.
Xuening Zhu
semanticscholar +1 more source
Tensor-Based Algorithms for Image Classification [PDF]
, 2019 Interest in machine learning with tensor networks has been growing rapidly in recent years. We show that tensor-based methods developed for learning the governing equations of dynamical systems from data can, in the same way, be used for supervised ...
Gelß, Patrick, Klus, Stefan
core +2 more sources
Error bounds for the convex loss Lasso in linear models
, 2017 In this paper we investigate error bounds for convex loss functions for the Lasso in linear models, by first establishing a gap in the theory with respect to the existing error bounds.
Mark H Hannay, P. Deléamont
semanticscholar +1 more source
, 2016 A new line of research on the lasso exploits the beautiful geometric fact that the lasso fit is the residual from projecting the response vector $y$ onto a certain convex polytope.
Harris, Naftali, Sepehri, Amir
core +1 more source