This work provides an elementary derivation of a single-sample post-stratified estimator—previously derived via transport equations—and proves a variance reduction relative to simple random sampling, expanding the applicability of implicit reparameterization and reducing its computational complexity.
In this thesis I develop methods for statistical inference when the distributions arising from complex biological systems are multi-modal, geometrically structured, and sometimes only defined up to a normalizing constant. I start from variational inference and, when analytic update equations are unavailable, move to bl...
The central limit theorem and consistency of the fully transcript-based sandwich covariance and intrinsic Wald region under local differential privacy are proved and the predicted decrease in estimation error and near-nominal coverage under moderate privacy is supported.
A momentum-based estimator with mini-batches is employed to track the function values of each level, which are subsequently used to construct momentum gradient estimators, and a batch-free method that incorporates a first-order approximation and a clipping technique for function value estimation is developed.
Wei Jiang, Rui Yan, Si-Fan Yang et al.· 0 citations
The proposed methodology delivers interpretable, simultaneous hyper-rectangular confidence regions that are statistically robust, memory-efficient, and strictly scalable for high-dimensional inference.
We develop a Gaussian comparison theory for posterior inference in non-Gaussian high-dimensional models. The framework allows for model misspecification and does not require the posterior distribution itself to be approximately Gaussian. Working directly with likelihood processes on separable function spaces, we establ...
Reinforcement learning and contextual bandit algorithms have become increasingly common in sequential decision-making applications. When these methods are deployed in high-stakes domains, there is growing interest not only in learning effective policies, but also in conducting statistical inference for quantities learn...
James Leiner, Aurélien F. Bibaut, Nathan Kallus et al.· 0 citations
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