搜索结果: 16-30 共查到“统计学 LASSO”相关记录41条 . 查询时间(0.164 秒)
The group fused Lasso for multiple change-point detection
Mines ParisTech CBIO, Fontainebleau, France Institut Curie, Paris, France INSERM U900, Paris, France
2011/7/7
We present the group fused Lasso for detection of multiple change-points shared by a set of co-occurring one-dimensional signals.
High-dimensional additive hazard models and the Lasso
Survival analysis Counting processes Censored data
2011/7/6
We consider a general high-dimensional additive hazard model in a non-asymptotic setting, including regression for censored-data.
Pivotal Estimation of Nonparametric Functions via Square-root Lasso
pLASSO Pivotal Estimation Square-root Lasso
2011/6/16
In a nonparametric linear regression model we study a variant of LASSO,
called pLASSO, which does not require the knowledge of the scaling parameter σ of the
noise or bounds for it. This work derive...
The LASSO for generic design matrices as a function of the relaxation parameter
linear regression LASSO relaxation parameter
2011/6/16
The LASSO is a variable subset selection procedure in statistical
linear regression based on ℓ1 penalization of the least-squares
operator. Its behavior crucially depends, both in practice and...
LASSO Methods for Gaussian Instrumental Variables Models
Methodology (stat.ME) Statistics Theory (math.ST)
2010/12/17
In this note, we propose to use sparse methods (e.g. LASSO, Post-LASSO, sqrt-LASSO, and Post-sqrt-LASSO) to form first-stage predictions and estimate optimal instruments in linear instrumental variabl...
The Lasso under Heteroscedasticity
Lasso Poisson-like Model Sign Consistency Heteroscedas-ticity
2010/11/9
The performance of the Lasso is well understood under the assumptions of the standard linear model with homoscedastic noise. However, in several appli-cations, the standard model does not describe the...
Exact block-wise optimization in group lasso for linear regression
Block coordinate descent convex optimization group LASSO sparse group LASSO
2010/10/19
The group lasso is a penalized regression method, used in regression problems where the covariates are partitioned into groups to promote sparsity at the group level. Existing methods for finding the ...
Group Lasso estimation of high-dimensional covariance matrices
Group Lasso ℓ 1 penalty high-dimensional covariance estimation basis expansion
2010/10/19
In this paper, we consider the Group Lasso estimator of the covariance matrix of a stochastic process corrupted by an additive noise. We propose to estimate the covariance matrix in a high-dimensiona...
Group-Lasso on Splines for Spectrum Cartography
Sparsity, splines (group-)Lasso field estimation cognitive radio sensing
2010/10/14
The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish...
Prediction and variable selection with the adaptive Lasso
adaptive Lasso prediction restricted eigenvalue thresholding variable selection
2010/3/9
We revisit the adaptive Lasso in a high-dimensional linear model,
and provide bounds for its prediction error and for its number of false positive
selections. We compare the adaptive Lasso with an “...
Thresholded Lasso for high dimensional variable selection and statistical estimation
Linear regression Lasso Gauss-Dantzig Selector 1 regularization 0 penalty multiple-stepprocedure ideal model selection
2010/3/10
Given n noisy samples with p dimensions, where n p, we show that the multi-step thresholding procedure based on the Lasso – we call it the Thresholded Lasso, can accurately estimate a sparse vector ...
Adaptive LASSO-type estimation for ergodic diffusion processes
discretely observed diffusion processes model selection oracle proper-ties random fields stochastic differential equations
2010/3/10
The LASSO is a widely used statistical methodology for simultaneous estimation
and variable selection. In the last years, many authors analyzed this technique from
a theoretical and applied point of...
We consider the group lasso penalty for the linear model. We note that
the standard algorithm for solving the problem assumes that the model
matrices in each group are orthonormal. Here we consider ...
Sparsity oracle inequalities for the Lasso
sparsity oracle inequalities Lasso penalized least squares dimension reduction aggregation mutual coherence
2009/9/16
This paper studies oracle properties of $ell_1$-penalized least squares in nonparametric regression setting with random design. We show that the penalized least squares estimator satisfies sparsity or...
Lasso type classifiers with a reject option
Bayes classifiers classification convex surrogate loss hinge loss large margin classifiers ℓ 1 penalties
2009/9/16
We consider the problem of binary classification where one can, for a particular cost, choose not to classify an observation. We present a simple proof for the oracle inequality for the excess risk of...