搜索结果: 1-15 共查到“理论统计学 LASSO”相关记录22条 . 查询时间(0.364 秒)
On Pattern Recovery of The Fused Lasso
Fused Lasso Non-asymptotic Pattern recovery Preconditioning
2016/1/20
We study the property of the Fused Lasso Signal Approximator(FLSA) for estimating a blocky signal sequence with additive noise.We transform the FLSA to an ordinary Lasso problem. By studying the prope...
For high dimensional supervised learning problems, often using problem specific assumptions can lead to greater accuracy. For problems with grouped covariates, which are believed to have sparse effect...
Learning interactions via hierarchical group-lasso regularization
hierarchical interaction computer intensive regression logistic
2015/8/21
We introduce a method for learning pairwise interactions in a linear regression or logistic regression model in a manner that satisfies strong hierarchy: whenever an interaction is estimated to be non...
A framework to characterize performance of LASSO algorithms
Noisy linear systems of equations LASSO SOCP ℓ 1 -optimization compressed sensing
2013/5/2
In this paper we consider solving \emph{noisy} under-determined systems of linear equations with sparse solutions. A noiseless equivalent attracted enormous attention in recent years, above all, due t...
The Lasso is a popular statistical tool invented by Robert Tibshirani for linear regression when the number of covariates is greater than or comparable to the number of observations. The validity of t...
The Lasso for High-Dimensional Regression with a Possible Change-Point
Lasso oracleine qualities sample splitting sparsity threshold models
2012/11/23
We consider a high-dimensional regression model with a possible change-point due to a covariate threshold and develop the Lasso estimator of regression coefficients as well as the threshold parameter....
The Lasso, correlated design, and improved oracle inequalities
compatibility correlation entropy high-dimensional model
2011/7/19
We study high-dimensional linear models and the $\ell_1$-penalized least squares estimator, also known as the Lasso estimator.
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 ...
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...