Profile MLE for the regime-switching threshold in null-recurrent diffusion converges at rate n^{-(1+γ)/2} to the arg sup of a doubly stochastic drifted Poisson process involving local time of oscillating Brownian motion.
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Causal Diffusion Model is the first diffusion-based method to produce full probabilistic counterfactual outcome distributions for sequential interventions in longitudinal data, showing 15-30% better distributional accuracy than prior methods on a tumor-growth simulator.
An extension of joint longitudinal-survival models enables Bayesian estimation of time-varying effects from repeatedly delivered treatments in micro-randomized trials for mobile health 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.
A Dirichlet process mixture model of GEV distributions for heterogeneous block maxima in extreme value analysis.
A divide-and-conquer median posterior inference method scales Gaussian process regression for multi-pollutant mixture health effects, demonstrated on 650,000 birthweight records with negative associations for traffic pollutants.
Approximates sum of correlated chi-squared variables as gamma and their difference as Variance-Gamma, with simulation tests showing good fit.
citing papers explorer
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Self-organized regime switching in null-recurrent dynamics
Profile MLE for the regime-switching threshold in null-recurrent diffusion converges at rate n^{-(1+γ)/2} to the arg sup of a doubly stochastic drifted Poisson process involving local time of oscillating Brownian motion.
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Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data
Causal Diffusion Model is the first diffusion-based method to produce full probabilistic counterfactual outcome distributions for sequential interventions in longitudinal data, showing 15-30% better distributional accuracy than prior methods on a tumor-growth simulator.
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Estimation of Time-Varying Treatment Effects in a Joint Model for Longitudinal and Recurrent Event Outcomes in Mobile Health Data
An extension of joint longitudinal-survival models enables Bayesian estimation of time-varying effects from repeatedly delivered treatments in micro-randomized trials for mobile health data.
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Testing Conditional Independence via the Spectral Generalized Covariance Measure: Beyond Euclidean 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.
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Bayesian Mixture Models for Heterogeneous Extremes
A Dirichlet process mixture model of GEV distributions for heterogeneous block maxima in extreme value analysis.
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Scalable Gaussian Process Regression Via Median Posterior Inference for Estimating Multi-Pollutant Mixture Health Effects
A divide-and-conquer median posterior inference method scales Gaussian process regression for multi-pollutant mixture health effects, demonstrated on 650,000 birthweight records with negative associations for traffic pollutants.
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A note on sum and difference of correlated chi-squared variables
Approximates sum of correlated chi-squared variables as gamma and their difference as Variance-Gamma, with simulation tests showing good fit.