We discuss the relevance of the recent machine learning (ML) literature for economics and
econometrics. First we discuss the differences in goals, methods, and settings between the …

With the advances in innovative instrumentation and various valuable applications, near-
infrared (NIR) spectroscopy has become a mature analytical technique in various fields …

We show that several machine learning estimators, including square-root least absolute
shrinkage and selection and regularized logistic regression, can be represented as …

Measurement error is ubiquitous in experimental work. It leads to imperfect statistical
controls, attenuated estimated effects of elicited behaviors, and biased correlations between …

Extraordinary amounts of data are being produced in many branches of science. Proven
statistical methods are no longer applicable with extraordinary large datasets due to …

This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make
rolling one‐minute‐ahead return forecasts using the entire cross‐section of lagged returns …

The goal of this paper is to contrast and survey the major advances in two of the most
commonly used high-dimensional techniques, namely, the Lasso and horseshoe …

We investigate implicit regularization schemes for gradient descent methods applied to
unpenalized least squares regression to solve the problem of reconstructing a sparse signal …

Regression models are a form of supervised learning methods that are important for
machine learning, statistics, and general data science. Despite the fact that classical …

We study the pricing problem faced by a firm that sells a large number of products, described
via a wide range of features, to customers that arrive over time. Customers independently …