DARTS adaptively selects prognostic covariates via budgeted Thompson sampling in sequential experiments, preserving randomization validity and achieving asymptotic coverage for the inverse-variance weighted estimator.
Academic press, 2014
2 Pith papers cite this work. Polarity classification is still indexing.
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A market choice model with random-size sampling from past customers is represented as an elephant random walk variant, with proofs of almost sure convergence of S_n/n and regime-dependent distributional limits for scaled S_n.
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DARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experiments
DARTS adaptively selects prognostic covariates via budgeted Thompson sampling in sequential experiments, preserving randomization validity and achieving asymptotic coverage for the inverse-variance weighted estimator.
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Elephant random walk with attributed steps and extractions of random sizes
A market choice model with random-size sampling from past customers is represented as an elephant random walk variant, with proofs of almost sure convergence of S_n/n and regime-dependent distributional limits for scaled S_n.