More Like this

1. Clustering Then Estimation of Spatio-Temporal Self-Exciting Processes

   https://doi.org/10.1287/ijoc.2022.0351  

   Zhang, Haoting; Zhan, Donglin; Anderson, James; Righter, Rhonda; Zheng, Zeyu (September 2024, INFORMS Journal on Computing)

   We propose a new estimation procedure for general spatio-temporal point processes that include a self-exciting feature. Estimating spatio-temporal self-exciting point processes with observed data is challenging, partly because of the difficulty in computing and optimizing the likelihood function. To circumvent this challenge, we employ a Poisson cluster representation for spatio-temporal self-exciting point processes to simplify the likelihood function and develop a new estimation procedure called “clustering-then-estimation” (CTE), which integrates clustering algorithms with likelihood-based estimation methods. Compared with the widely used expectation-maximization (EM) method, our approach separates the cluster structure inference of the data from the model selection. This has the benefit of reducing the risk of model misspecification. Our approach is computationally more efficient because it does not need to recursively solve optimization problems, which would be needed for EM. We also present asymptotic statistical results for our approach as theoretical support. Experimental results on several synthetic and real data sets illustrate the effectiveness of the proposed CTE procedure. 

   more » « less
2. Distributed robust estimation and inference with contaminated data

   https://doi.org/10.1214/26-EJS2492  

   Zhang, Peiliang; Zhou, Wen-Xin; Ren, Zhao (January 2026, Electronic Journal of Statistics)

   We study the problem of robust estimation and inference for contaminated data in distributed learning systems. Existing methods, such as those designed for Byzantine failures, typically assume that contamination is limited to a small subset of machines experiencing complete failure. In contrast, we address distributed partial contamination, where all machines may handle datasets containing some corrupted observations. By generalizing Huber’s 𝜖-contamination model to distributed settings, we propose a robust framework in which each machine’s dataset may be contaminated by a proportion 𝜖 of observations from an arbitrary distribution. In a linear model setting, we develop a communication-efficient M-estimator that achieves the optimal convergence rate of cen- tralized data. For robust inference, we introduce a distributed multiplier bootstrap method that requires no additional communication post-estimation while maintaining efficiency. To handle high contamination proportions, we present a debiasing procedure to mitigate bias. Extensive simulations demonstrate the robustness and scalability of our methods across diverse contamination scenarios. 

   more » « less
3. A Bayesian functional data model for surveys collected under informative sampling with application to mortality estimation using NHANES

   https://doi.org/10.1111/biom.13696  

   Parker, Paul A.; Holan, Scott H. (June 2023, Biometrics)

   Abstract Functional data are often extremely high‐dimensional and exhibit strong dependence structures but can often prove valuable for both prediction and inference. The literature on functional data analysis is well developed; however, there has been very little work involving functional data in complex survey settings. Motivated by physical activity monitor data from the National Health and Nutrition Examination Survey (NHANES), we develop a Bayesian model for functional covariates that can properly account for the survey design. Our approach is intended for non‐Gaussian data and can be applied in multivariate settings. In addition, we make use of a variety of Bayesian modeling techniques to ensure that the model is fit in a computationally efficient manner. We illustrate the value of our approach through two simulation studies as well as an example of mortality estimation using NHANES data. 

   more » « less
4. VarFA: {A} Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics

   Wang, Z.; Gu, Y.; Lan, A.; Baraniuk, R. (July 2020, Proceedings of the 13th International Conference on Educational Data Mining)

   Rafferty, A.; Whitehall, J.; Cristobal, R.; Cavalli-Sforza, V. (Ed.)

   We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estimation in the model's estimated factors. Such uncertainty information is useful, for example, for an adaptive testing scenario, where additional tests can be administered if the model is not quite certain about a students' skill level estimation. Traditional Bayesian inference methods that produce such uncertainty information are computationally expensive and do not scale to large data sets. VarFA utilizes variational inference which makes it possible to efficiently perform Bayesian inference even on very large data sets. We use the sparse factor analysis model as a case study and demonstrate the efficacy of VarFA on both synthetic and real data sets. VarFA is also very general and can be applied to a wide array of factor analysis models. 

   more » « less
5. Selective Inference with Distributed Data

   Liu, S; Panigrahi, S (January 2025, Journal of Machine Learning Research)

   Loh, Po-Ling (Ed.)

   When data are distributed across multiple sites or machines rather than centralized in one location, researchers face the challenge of extracting meaningful information without directly sharing individual data points. While there are many distributed methods for point estimation using sparse regression, few options are available for estimating uncertainties or conducting hypothesis tests based on the estimated sparsity. In this paper, we introduce a procedure for performing selective inference with distributed data. We consider a scenario where each local machine solves a lasso problem and communicates the selected predictors to a central machine. The central machine then aggregates these selected predictors to form a generalized linear model (GLM). Our goal is to provide valid inference for the selected GLM while reusing data that have been used in the model selection process. Our proposed procedure only requires low-dimensional summary statistics from local machines, thus keeping communication costs low and preserving the privacy of individual data sets. Furthermore, this procedure can be applied in scenarios where model selection is repeatedly conducted on randomly subsampled data sets, addressing the p-value lottery problem linked with model selection. We demonstrate the effectiveness of our approach through simulations and an analysis of a medical data set on ICU admissions. 

   more » « less