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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6 Pith papers cite this work. Polarity classification is still indexing.
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stat.ME 6years
2026 6verdicts
UNVERDICTED 6representative citing papers
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 functional Cox model is developed for interval-censored data using penalized maximum likelihood estimation via an EM algorithm, with proofs of consistency, asymptotic normality, and semiparametric efficiency, plus a global test for the functional covariate effect.
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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Functional Cox model for interval-censored data
A functional Cox model is developed for interval-censored data using penalized maximum likelihood estimation via an EM algorithm, with proofs of consistency, asymptotic normality, and semiparametric efficiency, plus a global test for the functional covariate effect.
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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.