Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.
Semiparametric Pseudo - Likelihoods in Generalized Linear Models With Nonignorable Missing Data
6 Pith papers cite this work. Polarity classification is still indexing.
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QATS is a new polylog-time approximate decoding procedure for HMMs that builds admissible state sequences by locally maximizing likelihoods over paths with at most three segments via adaptive ternary segmentation and cumulative sum storage.
Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory.
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.
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.
citing papers explorer
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Mean-Field Analysis of Latent Variable Process Models on Dynamically Evolving Graphs with Feedback Effects
Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.
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Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models
QATS is a new polylog-time approximate decoding procedure for HMMs that builds admissible state sequences by locally maximizing likelihoods over paths with at most three segments via adaptive ternary segmentation and cumulative sum storage.
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Comparing Two Categorical Gini Correlations with Applications to Classification Problems
Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
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Self-separated and self-connected models for mediator and outcome missingness in mediation analysis
Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory.
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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.
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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.