Search Results

Lasso is widely known as a sparse estimation method for regression coefficients in linear regression models, and the Alternating Direction Method of Multipliers (ADMM) is one of the representative computational methods for lasso. In this paper, we propose ...

We consider a contextual online learning (multi-armed bandit) problem with high-dimensional covariate x and decision y. The reward function to learn, f(x, y), does not have a particular parametric form. The literature has shown that the optimal regret is ...

Logistic regression is one of the most fundamental tools in predictive analytics. Graduate business analytics students are often familiarized with implementation of logistic regression using Python, R, SPSS, or other software packages. However, an ...

Asymmetric ν-twin Support vector regression (Asy-ν-TSVR) is an effective regression model in price prediction. However, there is a matrix inverse operation when solving its dual problem. It is well known that it may be not reversible, therefore a ...

We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers’ characteristics encoded as a d-dimensional feature vector. We assume a personalized demand model, parameters ...

We apply four machine learning methods to cross-sectional return prediction for hedge fund selection. We equip the forecast model with a set of idiosyncratic features, which are derived from historical returns of a hedge fund and capture a variety of fund-...

Much of the theory for the lasso in the linear model Y = Xβ^* + ε hinges on the quantity 2∥X^Τε∥∞/n, which we call the lasso's effective noise. Among other things, the effective noise plays an important role in finite-sample bounds for the lasso, the ...

This paper develops a fully discrete soft thresholding polynomial approximation over a general region, named Lasso hyperinterpolation. This approximation is an $\ell_1$-regularized discrete least squares approximation under the same conditions of ...

Estimating portfolio risk measures and classifying portfolio risk levels in real time are important yet challenging tasks. In this paper, we propose to build a logistic regression model using data generated in past simulation experiments and to use the ...

The $L_1 $ regularization (Lasso) has proven to be a versatile tool to select relevant features and estimate the model coefficients simultaneously and has been widely used in many research areas such as genomes studies, finance, and biomedical imaging. ...

Bayesian regularization is a central tool in modern‐day statistical and machine learning methods. Many applications involve high‐dimensional sparse signal recovery problems. The goal of our paper is to provide a review of the literature on penalty‐based ...

Comparison of geometry of a unit ball induced by Normal, Laplace, Cauchy and Horseshoe priors. image image

An approximate method for conducting resampling in Lasso, the l₁ penalized linear regression, in a semi-analytic manner is developed, whereby the average over the resampled datasets is directly computed without repeated numerical sampling, thus enabling ...

The state of the art in bio-medical technologies has produced many genomic, epigenetic, transcriptomic, and proteomic data of varied types across different biological conditions. Historically, it has always been a challenge to produce new ways to ...

In this article, based on of open legislative data mining, we propose a methodology to create a model capable of indicating which characteristics have a positive or negative impact on the approval of a bill by the Chamber of Deputies. Added to the ...

The use of natural language processing (NLP) is gaining popularity in software engineering. In order to correctly perform NLP, we must pre-process the textual information to separate natural language from other information, such as log messages, that are ...

We present upper and lower bounds for the prediction error of the Lasso. For the case of random Gaussian design, we show that under mild conditions the prediction error of the Lasso is up to smaller order terms dominated by the prediction error of its ...

We develop a fast and robust algorithm for solving large-scale convex composite optimization models with an emphasis on the $\ell_1$-regularized least squares regression (lasso) problems. Despite the fact that there exist a large number of solvers in the ...

Black box variational inference (BBVI) is a recently proposed estimation method for parameters of statistical models. BBVI is an order of magnitude faster than Markov chain Monte Carlo (MCMC). The computation of BBVI is similar to maximum a posteriori ...

In high-dimensional data settings where pï źï źï źn, many penalized regularization approaches were studied for simultaneous variable selection and estimation. However, with the existence of covariates with weak effect, many existing variable selection ...

We extend the adaptive regression spline model by incorporating saturation, the natural requirement that a function extend as a constant outside a certain range. We fit saturating splines to data via a convex optimization problem over a space of ...