Initiates finite-sample theory for differentially private hypothesis testing in survival analysis, with private tests for Cox models and cumulative hazards plus minimax bounds.
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8 Pith papers cite this work. Polarity classification is still indexing.
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Ultrametric graphons model hierarchical community networks and yield closed-form Laplacian spectra that approximate those of sampled random graphs with high probability as hierarchy depth grows.
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.
Unified framework relaxes spectral constraints and provides parameter-free guarantees linking practical algorithms to MLE for latent space network models.
Proves consistency and asymptotic normality of the Whittle estimator for stationary multivariate Hawkes processes under the spectral radius condition alone, plus a frequency-domain test for subprocess independence.
A Bayesian mixed Hawkes process with Weibull baseline intensity and random effects is developed to model seizure clustering and heterogeneity in focal epilepsy from the Human Epilepsy Project data.
citing papers explorer
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Differentially private hypothesis testing in survival analysis
Initiates finite-sample theory for differentially private hypothesis testing in survival analysis, with private tests for Cox models and cumulative hazards plus minimax bounds.
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Ultrametric Graphons and Hierarchical Community Networks: Spectral Theory and Applications
Ultrametric graphons model hierarchical community networks and yield closed-form Laplacian spectra that approximate those of sampled random graphs with high probability as hierarchy depth grows.
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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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Bridging Theory and Practice: Statistical Inference for Latent Space Models of Networks
Unified framework relaxes spectral constraints and provides parameter-free guarantees linking practical algorithms to MLE for latent space network models.
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Spectral analysis of multivariate stationary Hawkes processes
Proves consistency and asymptotic normality of the Whittle estimator for stationary multivariate Hawkes processes under the spectral radius condition alone, plus a frequency-domain test for subprocess independence.
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A Mixed Self-Exciting Process to Model Epileptic Seizures
A Bayesian mixed Hawkes process with Weibull baseline intensity and random effects is developed to model seizure clustering and heterogeneity in focal epilepsy from the Human Epilepsy Project data.
- Sensitivity analysis for causal mediation: bridge score, sharp sensitivity bounds, and calibration
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