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Found 9 papers, 0 papers with code
Data-Driven Tuning Parameter Selection for High-Dimensional Vector Autoregressions
Lasso-type estimators are routinely used to estimate high-dimensional time series models.
Regularizing Discrimination in Optimal Policy Learning with Distributional Targets
A decision maker typically (i) incorporates training data to learn about the relative effectiveness of the treatments, and (ii) chooses an implementation mechanism that implies an "optimal" predicted outcome distribution according to some target functional.
A Ridge-Regularised Jackknifed Anderson-Rubin Test
We consider hypothesis testing in instrumental variable regression models with few included exogenous covariates but many instruments -- possibly more than the number of observations.
Treatment recommendation with distributional targets
We study the problem of a decision maker who must provide the best possible treatment recommendation based on an experiment.
Functional Sequential Treatment Allocation with Covariates
We consider a multi-armed bandit problem with covariates.
Functional Sequential Treatment Allocation
Then, we introduce and study the Functional Upper Confidence Bound (F-UCB) policy, which interweaves exploration and exploitation and is thus not of the ETC type.
Optimal sequential treatment allocation
We study a problem in which the policy maker is not only interested in the expected cumulative welfare but is also concerned about the uncertainty/risk of the treatment outcomes.
Oracle Inequalities for Convex Loss Functions with Non-Linear Targets
We give two examples of loss functions covered by our framework and show how penalized nonparametric series estimation is contained as a special case and provide a finite sample upper bound on the mean square error of the elastic net series estimator.
Oracle Inequalities for High Dimensional Vector Autoregressions
We then give conditions under which the Adaptive LASSO reveals the correct sparsity pattern asymptotically.
