arXiv research

The paper examines Adaptive Lasso and Transfer Lasso, highlighting their differences and proposing a new method.

Paper proposes a method to efficiently estimate structural breaks in cointegrating regressions.

Bayesian Lasso Sparse model provides sparse estimates in linear and nonlinear regression.

New bounds for Lasso and Group Lasso in high dimensions derived.

We propose an improved LASSO estimation technique based on Stein-rule. We shrink classical LASSO estimator using preliminary test, shrinkage, and positive-rule shrinkage principle. Simulation results have been carried out for various configurations of correlation coefficients ($r$), size of the parameter vector ($β$), …

While considerable advances have been made in estimating high-dimensional structured models from independent data using Lasso-type models, limited progress has been made for settings when the samples are dependent. We consider estimating structured VAR (vector auto-regressive models), where the structure can be capture…

Improves LASSO by scaling to reduce bias and enhance model selection.

Javanmard and Montanari propose a debiased estimator for high-dimensional regression.

Study improves statistical inference for CATEs using Lasso and DML.

New GLS estimator handles high-dimensional data with autocorrelated errors.

New weighted Lasso estimates improve logistic regression performance with measurement error.

Bayesian approach improves network lasso for multi-task learning.

Optimizes UoI_LASSO and UoI_VAR for scalable statistical estimation of large datasets.

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 property of the design matrix in the transformed Lasso problem, we find that the irrepresentable condition might not hold, …

The fused lasso is analyzed for high-dimensional piecewise-constant regression coefficients.

In high dimension, it is customary to consider Lasso-type estimators to enforce sparsity. For standard Lasso theory to hold, the regularization parameter should be proportional to the noise level, yet the latter is generally unknown in practice. A possible remedy is to consider estimators, such as the Concomitant/Scale…

A new estimator corrects bias in high-dimensional predictive regressions.

Exponential Lasso improves Lasso's robustness to outliers and heavy-tailed noise.

A new method speeds up overlapping group lasso computations.

We study the distribution of the adaptive LASSO estimator (Zou (2006)) in finite samples as well as in the large-sample limit. The large-sample distributions are derived both for the case where the adaptive LASSO estimator is tuned to perform conservative model selection as well as for the case where the tuning results…

Proposes a new method for estimating treatment effects using outcome highly adaptive lasso.

The Lasso is suboptimal in sparse linear regression due to design matrix constraints.

The least absolute shrinkage and selection operator (lasso) and ridge regression produce usually different estimates although input, loss function and parameterization of the penalty are identical. In this paper we look for ridge and lasso models with identical solution set. It turns out, that the lasso model with shri…

In this paper we present a regression based model for day-ahead electricity spot prices. We estimate the considered linear regression model by the lasso estimation method. The lasso approach allows for many possible parameters in the model, but also shrinks and sparsifies the parameters automatically to avoid overfitti…

We simplify debiasing of LASSO estimates for practical computation.

We propose a new method of learning a sparse nonnegative-definite target matrix. Our primary example of the target matrix is the inverse of a population covariance or correlation matrix. The algorithm first estimates each column of the target matrix by the scaled Lasso and then adjusts the matrix estimator to be symmet…

This paper considers a multiple regression model and compares, under full model hypothesis, analytically as well as by simulation, the performance characteristics of some popular penalty estimators such as ridge regression, LASSO, adaptive LASSO, SCAD, and elastic net versus Least Squares Estimator, restricted estimato…

New proof shows faster convergence rate for robust estimation with Lasso in adversarially contaminated outputs.

The Mismatch Principle improves Lasso robustness to model uncertainties.

The paper develops estimators for variance in graph structures using fused lasso.

We consider the task of estimating a Gaussian graphical model in the high-dimensional setting. The graphical lasso, which involves maximizing the Gaussian log likelihood subject to an l1 penalty, is a well-studied approach for this task. We begin by introducing a surprising connection between the graphical lasso and hi…

We propose a minimum distance estimation method for robust regression in sparse high-dimensional settings. The traditional likelihood-based estimators lack resilience against outliers, a critical issue when dealing with high-dimensional noisy data. Our method, Minimum Distance Lasso (MD-Lasso), combines minimum distanc…

Study examines Lasso performance in high-dimensional MoE models.

Selective inference for group lasso estimators across various distributions and covariates.

Paper analyzes adaptive Lasso for high-dimensional diffusion processes, improving support recovery and bias.

New algorithm speeds up Lasso computation by proving faster convergence.

New robust estimator for high-dimensional data with outliers and leverage points.

Robust Lasso-Zero handles missing covariates and sparse corruptions.

New method handles correlated and repeated measurements using smoothed multivariate square-root Lasso.

In high dimensional settings, sparse structures are crucial for efficiency, both in term of memory, computation and performance. It is customary to consider $\ell_1$ penalty to enforce sparsity in such scenarios. Sparsity enforcing methods, the Lasso being a canonical example, are popular candidates to address high dim…

The $\ell_1$-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weigh…

c-lasso is a Python tool for robust and sparse regression with linear constraints.

Paper develops a new estimator for high-dimensional panel data with common shocks.

A new algorithm improves GLasso for sparse precision matrix estimation.

Many theoretical results on estimation of high dimensional time series require specifying an underlying data generating model (DGM). Instead, along the footsteps of~\cite{wong2017lasso}, this paper relies only on (strict) stationarity and $β$-mixing condition to establish consistency of lasso when data comes from a $β…

Shrinkage algorithms are of great importance in almost every area of statistics due to the increasing impact of big data. Especially time series analysis benefits from efficient and rapid estimation techniques such as the lasso. However, currently lasso type estimators for autoregressive time series models still focus …

Paper proposes new estimators for error variance in high-dimensional linear models.

This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger order of magnitude than the sample …