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Alexandre Belloni works across optimization, econometrics and high-dimensional statistics. His Chicago Booth profile describes research connecting statistical inference with decisions under limited information.^[ref]

With Victor Chernozhukov and Lie Wang, he formulated the Square-root Lasso as a convex conic optimization problem. This connects a statistically pivotal tuning rule with algorithms for convex optimization, giving a computable sparse regression estimator without a preliminary estimate of noise scale.^[ref]

David Pollard is a statistician whose research and exposition concern weak convergence, empirical processes and stochastic processes. Yale's institutional profile identifies these specialties, together with statistical asymptotics and econometrics.^[ref]

A recurring scientific issue in his writing is how bounds for one random quantity become simultaneous bounds for a whole indexed family. His Measurability Woes by David Pollard explains why pointwise almost-sure agreement does not automatically provide a single exceptional set valid at every index. That distinction matters when a supremum or an entire sample path enters an argument. His author page lists Convergence of Stochastic Processes (1984) and Empirical Processes: Theory and Applications (1990), followed by broader notes on probability methods.^[ref][ref]

Econometrics uses statistical models to quantify economic relationships and to assess predictions or causal claims from economic data. The choice of variables and the mechanism generating observations matter as much as estimating the model coefficients.

For example, in a linear regression of log earnings on years of schooling, a slope $0.08$ corresponds to a fitted earnings ratio $e^{0.08}\approx 1.083$ for one additional year. Interpreting that ratio as a causal effect needs more than a good fit: prior attainment or family circumstances may affect both schooling and earnings, creating confounding. Randomization, suitable instrumental variables or a credible natural experiment can support causal interpretation under additional assumptions. The numerical regression coefficient alone supplies an association within its model, not those assumptions.

Lajos Horváth works on mathematical statistics, time series and econometrics. His University of Utah profile identifies these research interests.^[ref]

His joint paper with Piotr Kokoszka and Ron Reeder studies comparisons of mean curves in a functional time series. Its Hilbert space formulation yields Gaussian limits and a test based on the distance between two estimated mean functions, retaining dependence within each sample.^[ref]

Victor Chernozhukov works on econometrics and statistical inference with machine learning. With Alexandre Belloni and Lie Wang, he developed the Square-root Lasso, whose penalty calibration does not require knowing the noise scale.^[ref]

He also coauthored work on double machine learning. Predicting an outcome accurately does not by itself estimate a causal effect accurately: errors in fitted nuisance functions can bias the target parameter. An orthogonal estimating equation reduces first-order sensitivity to those errors, while cross-fitting separates training observations from score evaluation. Valid confidence intervals still require the paper's identification and convergence assumptions.^[ref]