Proposes ERHT-CC test based on spatial median and spatial-sign covariance with Cauchy aggregation over ridge parameters, deriving asymptotic normality and local power under elliptical symmetry.
hub
Finley, and Alan E
18 Pith papers cite this work. Polarity classification is still indexing.
18
Pith papers citing it
hub tools
citation-role summary
background 1
method 1
citation-polarity summary
representative citing papers
A nonparametric estimator of the regimen-response curve for stochastic JITAIs on distal outcomes is developed, with weak convergence to a Gaussian process and asymptotic theory for the optimizing policy.
Exact likelihood for continuous latent space models under snowball sampling reduces to closed form via conditional edge independence, enabling stochastic EM for unbiased inference on networks like patent co-inventors.
A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.
Joint calibrated LTMLE integrates LTMLE with joint calibrated weights to improve finite-sample efficiency and robustness to misspecification for per-protocol effect estimation in target trial emulation.
A recursive Riesz representer-based targeted minimum loss estimation procedure unifies asymptotically efficient estimation of causal estimands such as time-varying treatment effects and mediation effects.
Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.
A new decay-adjusted spatio-temporal model improves estimation of neglected tropical disease prevalence by explicitly accounting for the waning impact of mass drug administration in sparse survey data.
A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.
Differential privacy reduces algorithmic collective action effectiveness, with formal lower bounds on success probability depending on collective size and privacy parameters, plus experimental verification on neural nets.
Develops an NNGP spatio-temporal model with SMC squared inference for haplotype frequency estimation from pooled genetic data, demonstrated on 3- and 6-marker antimalarial resistance datasets in Africa.
P-K-GCN integrates continuous spline GCN, Koopman linearization, and physics augmentation for spatiotemporal super-resolution on irregular geometries, claiming theoretical error reduction via Rademacher complexity bounds and superior accuracy on cardiac electrodynamics.
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.
A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.
A random-forest surrogate for likelihood change under tree moves enables delayed-acceptance SMC that cuts expensive likelihood evaluations while preserving posterior estimates on simulated and real phylogenetic data.
A physics-constrained cGAN is trained as an image-to-image translator on remote-sensing layers to recover spatial sensitivities of urban land-use change to macroeconomic indicators via backpropagation gradients.
FASE pairs a spatiotemporal graph neural network and multivariate Hawkes process for crime prediction with a fairness-constrained linear program for patrol allocation, showing that allocation fairness holds in simulation but a 3.5 percentage point detection gap between minority and non-minority ZIPs
citing papers explorer
-
A decay-adjusted spatio-temporal model to account for the impact of mass drug administration on neglected tropical disease prevalence
A new decay-adjusted spatio-temporal model improves estimation of neglected tropical disease prevalence by explicitly accounting for the waning impact of mass drug administration in sparse survey data.
-
Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients
A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.