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Functional Clustering for Longitudinal Associations between Social Determinants of Health and Stroke Mortality in the US

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arXiv 2024年

作者： Luo, Fangzhi Tan, Jianbin Zhang, Donglan Huang, Hui Shen, Ye Department of Epidemiology and Biostatistics College of Public Health University of Georgia AthensGA United States Department of Biostatistics and Bioinformatics School of Medicine Duke University DurhamNC United States Department of Foundations of Medicine NYU Grossman Long Island School of Medicine MineolaNY United States Center for Applied Statistics School of Statistics Renmin University of China Beijing China

Understanding the longitudinally changing associations between Social Determinants of Health (SDOH) and stroke mortality is essential for effective stroke management. Previous studies have uncovered significant regional disparities in the associations between SDOH and stroke mortality. However, existing studies have not utilized longitudinal associations to develop data-driven methods for regional division in stroke control. To fill this gap, we propose a novel clustering method to analyze SDOH-stroke mortality associations across US counties. To enhance the interpretability of the clustering outcomes, we introduce a novel regularized expectation-maximization algorithm equipped with sparsity- and smoothness-pursued penalties, aiming at simultaneous clustering and variable selection in longitudinal associations. As a result, we can identify key SDOH that contributes to longitudinal changes in stroke mortality. This facilitates the clustering of US counties based on the associations between these SDOH and stroke mortality. The effectiveness of our proposed method is demonstrated through extensive numerical studies. By applying our method to longitudinal data on SDOH and stroke mortality at the county level, we identify 18 important SDOH for stroke mortality and divide the US counties into two clusters based on these selected SDOH. Our findings unveil complex regional heterogeneity in the longitudinal associations between SDOH and stroke mortality, providing valuable insights into region-specific SDOH adjustments for mitigating stroke mortality. © 2024, CC BY-NC-ND.

关键词： expectation maximization algorithm

Identification and Uncertainty Quantification of Hybrid Models with Incomplete Data for Chemometric Applications

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SSRN

SSRN 2024年

作者： Cong, Hongri Wang, Bo Chen, Tao School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Chemistry and Chemical Engineering University of Surrey GuildfordGU2 7XH United Kingdom

Hybrid models, which integrate multiple modelling approaches, are crucial in accurately capturing complex systems in chemical engineering. The identification and quantification of uncertainties in these models significantly influence their predictive performance and generalizability. In this paper, we introduce a novel hybrid model framework designed to work with incomplete datasets, a common scenario in chemometrics. For model identification and uncertainty quantification, two methods, namely the Monte Carlo expectation-maximization (MCEM) algorithm and the Plug-in method, are discussed and compared. Through extensive simulations and a real experimental data in modelling partition coefficients of chemicals, we demonstrate the usefulness of the model and the effectiveness of the proposed methods for model identification and uncertainty quantification, providing valuable insights into their practical applications in chemometrics. © 2024, The Authors. All rights reserved.

关键词： expectation maximization algorithm

Many Hands Make Light Work: Group Influence maximization in Evolving Social Networks

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IEEE Transactions on Big Data 2024年

作者： Ma, Yuliang Chen, Yu Wei, Peng Yuan, Ye Wang, Guoren Jiang, Zhong-Zhong Northeastern University College of Information Science and Engineering Shenyang110167 China Northeastern University College of Information Science and Engineering Shenyang110189 China Shandong Branch Industrial and Commercial Bank of China Jinan250001 China Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China Northeastern University School of Business Administration The Institute of Behavioral and Service Operations Management Shenyang110167 China

As the adage 'many hands make light work' suggests, collaborative influence often surpasses individual influence. Inspired by this insight, we undertook a study on group influence maximization in evolving social networks, which is applicable to domains such as social media marketing and financial risk management. Our goal is to reveal how collaborative influence propagates in dynamic settings. Existing research concentrates predominantly on static networks and overlooks the dynamics of evolving social structures. Recognizing the limitations of current influence propagation models for our specific issue, we introduce an innovative model rooted in user behaviors. It considers temporal aspects, and we also suggest a methodology for assessing influence propagation probabilities based on both user behaviors and duration. We introduce an algorithm for extracting user groups using community search, improving efficiency through supergraph construction. Additionally, we present an influence maximization algorithm based on group dynamics with a 3-degree propagation framework. Recognizing diminishing influence, a 3-degree truncation strategy effectively enhances the group influence propagation efficiency. This approach efficiently captures the influence spread and accelerates convergence, boosting the search efficiency. Finally, we conducted comprehensive experiments on real-world and synthetic datasets. The results distinctly highlight the superiority of the proposed algorithms. © 2015 IEEE.

关键词： expectation maximization algorithm

Multi-objective Reinforcement Learning with Nonlinear Preferences: Provable Approximation for Maximizing Expected Scalarized Return

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arXiv 2023年

作者： Peng, Nianli Tian, Muhang Fain, Brandon Harvard University CambridgeMA United States Duke University DurhamNC United States

We study multi-objective reinforcement learning with nonlinear preferences over trajectories. That is, we maximize the expected value of a nonlinear function over accumulated rewards (expected scalarized return or ESR) in a multi-objective Markov Decision Process (MOMDP). We derive an extended form of Bellman optimality for nonlinear optimization that explicitly considers time and current accumulated reward. Using this formulation, we describe an approximation algorithm for computing an approximately optimal non-stationary policy in pseudopolynomial time for smooth scalarization functions with a constant number of rewards. We prove the approximation analytically and demonstrate the algorithm experimentally, showing that there can be a substantial gap between the optimal policy computed by our algorithm and alternative baselines. © 2023, CC BY-NC-SA.

关键词： expectation maximization algorithm

High-dimensional variable clustering based on maxima of a weakly dependent random process

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arXiv 2023年

作者： Boulin, Alexis Di Bernardino, Elena Laloë, Thomas Toulemonde, Gwladys Université Côte d’Azur CNRS LJAD France Univ Montpellier CNRS Montpellier France Inria Lemon France

We propose a new class of models for variable clustering called Asymptotic Independent block (AI-block) models, which defines population-level clusters based on the independence of the maxima of a multivariate stationary mixing random process among clusters. This class of models is identifiable, meaning that there exists a maximal element with a partial order between partitions, allowing for statistical inference. We also present an algorithm depending on a tuning parameter that recovers the clusters of variables without specifying the number of clusters a priori. Our work provides some theoretical insights into the consistency of our algorithm, demonstrating that under certain conditions it can effectively identify clusters in the data with a computational complexity that is polynomial in the dimension. A data-driven selection method for the tuning parameter is also proposed. To further illustrate the significance of our work, we applied our method to neuroscience and environmental real-datasets. These applications highlight the potential and versatility of the proposed *** Codes 60G70, 62H05, 62M99 © 2023, CC BY.

关键词： expectation maximization algorithm

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Geometric Sampling

arXiv 2023年

作者： Panahbehagh, Bardia Faculty of Mathematical Sciences and Computer Kharazmi University Tehran Iran

This paper introduces an innovative and intuitive finite population sampling method that have been developed using a unique geometric framework. In this approach, I represent first-order inclusion probabilities as bars on a two-dimensional graph. By manipulating the positions of these bars, researchers can create a wide range of different sampling designs. This geometric visualization of sampling designs not only leads to increased creativity for researchers to provide new efficient designs but also eliminates the need for complex mathematical algorithms. This novel approach holds significant promise for tackling complex challenges in sampling, such as maximizing entropy and achieving an optimal design. By applying a version of the greedy best-first search algorithm to this geometric approach for finding an optimal design, I have demonstrated the potential for integrating intelligent algorithms into finite population sampling. Copyright © 2023, The Authors. All rights reserved.

关键词： expectation maximization algorithm

Extending Cluster-Weighted Factor Analyzers for multivariate prediction and high-dimensional interpretability

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arXiv 2024年

作者： Qin, Xiaoke Martella, Francesca Subedi, Sanjeena School of Mathematics and Statistics Carleton University ON Canada Department of Statistical Sciences Sapienza University of Rome Italy

Cluster-weighted factor analyzers (CWFA) are a versatile class of mixture models designed to estimate the joint distribution of a random vector that includes a response variable along with a set of explanatory variables. They are particularly valuable in situations involving high dimensionality. This paper enhances CWFA models in two notable ways. First, it enables the prediction of multiple response variables while considering their potential interactions. Second, it identifies factors associated with disjoint groups of explanatory variables, thereby improving interpretability. This development leads to the introduction of the multivariate cluster-weighted disjoint factor analyzers (MCWDFA) model. An alternating expectation-conditional maximization algorithm is employed for parameter estimation. The effectiveness of the proposed model is assessed through an extensive simulation study that examines various scenarios. The proposal is applied to crime data from the United States, sourced from the UCI Machine Learning Repository, with the aim of capturing potential latent heterogeneity within communities and identifying groups of socioeconomic features that are similarly associated with factors predicting crime rates. Results provide valuable insights into the underlying structures influencing crime rates which may potentially be helpful for effective cluster-specific policymaking and social *** Codes 62H30 © 2024, CC BY.

关键词： expectation maximization algorithm

A novel family of beta mixture models for the differential analysis of DNA methylation data: an application to prostate cancer

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arXiv 2022年

作者： Majumdar, Koyel Silva, Romina Perry, Antoinette Sabrina Watson, Ronald William Rau, Andrea Jaffrezic, Florence Murphy, Thomas Brendan Gormley, Isobel Claire School of Mathematics and Statistics University College Dublin Ireland School of Medicine University College Dublin Ireland Conway Institute of Biomedical and Biomolecular Research University College Dublin Ireland School of Biology and Environmental Science University College Dublin Ireland INRAE UMR1313 AgroParisTech GABI Université Paris-Saclay France

Identifying differentially methylated cytosine-guanine dinucleotide (CpG) sites between benign and tumour samples can assist in understanding disease. However, differential analysis of bounded DNA methylation data often requires data transformation, reducing biological interpretability. To address this, a family of beta mixture models (BMMs) is proposed that (i) objectively infers methylation state thresholds and (ii) identifies differentially methylated CpG sites (DMCs) given untransformed, beta-valued methylation data. The BMMs achieve this through model-based clustering of CpG sites and by employing parameter constraints, facilitating application to different study settings. Inference proceeds via an expectation-maximisation algorithm, with an approximate maximization step providing tractability and computational feasibility. Performance of the BMMs is assessed through thorough simulation studies, and the BMMs are used for differential analyses of DNA methylation data from a prostate cancer study. Intuitive and biologically interpretable methylation state thresholds are inferred and DMCs are identified, including those related to genes such as GSTP1, RASSF1 and RARB, known for their role in prostate cancer development. Gene ontology analysis of the DMCs revealed significant enrichment in cancer-related pathways, demonstrating the utility of BMMs to reveal biologically relevant insights. An R package betaclust facilitates widespread use of BMMs. © 2022, CC BY.

关键词： expectation maximization algorithm

Non-linear Welfare-Aware Strategic Learning

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arXiv 2024年

作者： Xie, Tian Zhang, Xueru Department of Computer Science and Engineering The Ohio State University United States

This paper studies algorithmic decision-making in the presence of strategic individual behaviors, where an ML model is used to make decisions about human agents and the latter can adapt their behavior strategically to improve their future data. Existing results on strategic learning have largely focused on the linear setting where agents with linear labeling functions best respond to a (noisy) linear decision policy. Instead, this work focuses on general non-linear settings where agents respond to the decision policy with only "local information" of the policy. Moreover, we simultaneously consider objectives of maximizing decision-maker welfare (model prediction accuracy), social welfare (agent improvement caused by strategic behaviors), and agent welfare (the extent that ML underestimates the agents). We first generalize the agent best response model in previous works to the non-linear setting and then investigate the compatibility of welfare objectives. We show the three welfare can attain the optimum simultaneously only under restrictive conditions which are challenging to achieve in non-linear settings. The theoretical results imply that existing works solely maximizing the welfare of a subset of parties usually diminish the welfare of others. We thus claim the necessity of balancing the welfare of each party in non-linear settings and propose an irreducible optimization algorithm suitable for general strategic learning. Experiments on synthetic and real data validate the proposed algorithm. © 2024, CC BY.

关键词： expectation maximization algorithm