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Lasso and Friends
Regularized linear models are generalized linear regression models with a penalty for large coefficients to regulate the bias-variance tradeoff. For...
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Bayesian fused lasso modeling via horseshoe prior
Bayesian fused lasso is one of the sparse Bayesian methods, which shrinks both regression coefficients and their successive differences...
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Variational Bayesian Lasso for spline regression
This work presents a new scalable automatic Bayesian Lasso methodology with variational inference for non-parametric splines regression that can...
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Coordinate descent algorithm for generalized group fused Lasso
We deal with a model with discrete varying coefficients to consider modeling for heterogeneity and clustering for homogeneity, and estimate the...
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A New Type of LASSO Regression Model with Cauchy Noise
Many datasets have heavy-tailed behavior, and classical penalized models are not appropriate for them. To treat this problem, we propose a penalized...
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Bayesian penalization for explanatory cognitive diagnostic models: covariate DINA model and covariate LCDM with the lasso prior
Diagnostic assessment data obtained from online learning platforms for schools are typically accompanied by student background variables and item...
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Generalized regression estimators with concave penalties and a comparison to lasso type estimators
The generalized regression (GREG) estimator is usually used in survey sampling when incorporating auxiliary information. Generally, not all available...
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LASSO regularization within the LocalGLMnet architecture
Deep learning models have been very successful in the application of machine learning methods, often out-performing classical statistical models such...
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Spatio-temporal clustering analysis using generalized lasso with an application to reveal the spread of Covid-19 cases in Japan
This study addressed the issue of determining multiple potential clusters with regularization approaches for the purpose of spatio-temporal...
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Sparse and debiased lasso estimation and inference for high-dimensional composite quantile regression with distributed data
We consider the data are inherently distributed and focus on statistical learning in the presence of heavy-tailed and/or asymmetric errors. The...
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Bayesian Elastic-Net and Fused Lasso for Semiparametric Structural Equation Models: An Application in Understanding the Relationship Between Alcohol Morbidity and Other Substance Abuse Factors Among American Youth
In contemporary times, high-dimensional datasets have become increasingly prevalent, owing to the expansion and complexity of data collection...
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Lasso-based variable selection methods in text regression: the case of short texts
Communication through websites is often characterised by short texts, made of few words, such as image captions or tweets. This paper explores the...
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Robust Moderately Clipped LASSO for Simultaneous Outlier Detection and Variable Selection
Outlier detection has become an important and challenging issue in high-dimensional data analysis due to the coexistence of data contamination and...
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The Lasso with Structured Design and Entropy of (Absolute) Convex Hulls
The paper [10] shows bounds for the prediction error of the Lasso. They use projection arguments to establish non-adaptive as well as adaptive...
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A new active zero set descent algorithm for least absolute deviation with generalized LASSO penalty
A new active zero set descent algorithm is proposed for least absolute deviation (LAD) problems with generalized LASSO penalty. Zero set contains the...
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Coordinate descent algorithm of generalized fused Lasso logistic regression for multivariate trend filtering
Generalized fused Lasso (GFL) is an extension of fused Lasso and performs multivariate trend filtering based on adjacent information among...
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Active-set based block coordinate descent algorithm in group LASSO for self-exciting threshold autoregressive model
Group LASSO (gLASSO) estimator has been recently proposed to estimate thresholds for the self-exciting threshold autoregressive model, and a group...
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Asymptotic of the number of false change points of the fused lasso signal approximator
It is well-known that the fused lasso signal approximator (FLSA) is inconsistent in change point detection under the presence of staircase blocks in...
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Robust Estimation Through Preliminary Testing Based on the LAD-LASSO
The least absolute deviation (LAD) estimator is an alternative to the ordinary least squares estimator when some outliers exist, or the error term in...