Introduces conditional autoregressive models for spatially dependent functional data with consistent covariance estimation via conditional centering and superconsistent, asymptotically normal estimation of the spatial dependence parameter under an expanding lattice.
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Derives a unified UMVCUE for general subpopulation selection rules in adaptive enrichment designs based on sample space partition.
A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
Develops tests for no dependence and partial effects in global Fréchet regression using random multipliers for null distributions and the Cauchy combination method, with consistency results and simulations on networks and spheres.
A new bootstrap goodness-of-fit test for the logistic propensity score model under nonignorable missing data, based on marginal sum-of-squared residuals, with asymptotic size and power guarantees.
citing papers explorer
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A new class of functional conditional autoregressive models
Introduces conditional autoregressive models for spatially dependent functional data with consistent covariance estimation via conditional centering and superconsistent, asymptotically normal estimation of the spatial dependence parameter under an expanding lattice.
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Unbiased estimation in two-stage adaptive enrichment designs
Derives a unified UMVCUE for general subpopulation selection rules in adaptive enrichment designs based on sample space partition.
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In-Sample Evaluation of Subgroups Identified by Generic Machine Learning
A conditional adaptive perturbation approach enables valid in-sample inference for machine learning-identified subgroups with nonregular boundaries via triple robustness.
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Inference for Fr\'echet Regression
Develops tests for no dependence and partial effects in global Fréchet regression using random multipliers for null distributions and the Cauchy combination method, with consistency results and simulations on networks and spheres.
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A goodness-of-fit test for the logistic propensity score model under nonignorable missing data
A new bootstrap goodness-of-fit test for the logistic propensity score model under nonignorable missing data, based on marginal sum-of-squared residuals, with asymptotic size and power guarantees.