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arXiv 2025年

作者： Cao, Jiahao Yang, Hou-Cheng Hu, Guanyu Center for Spatial Temporal Modeling for Applications in Population Sciences Department of Biostatistics and Data Science The University of Texas Health Science Center HoustonTX United States Department of Statistics Florida State University Florida United States

Basketball analytics has significantly advanced our understanding of the game, with shot selection emerging as a critical factor in both individual and team performance. With the advent of player tracking technologies, a wealth of granular data on shot attempts has become available, enabling a deeper analysis of shooting behavior. However, modeling shot selection presents unique challenges due to the spatial and contextual complexities influencing shooting decisions. This paper introduces a novel approach to the analysis of basketball shot data, focusing on the spatial distribution of shot attempts, also known as intensity surfaces. We model these intensity surfaces using a Functional Bayesian Additive Regression Trees (FBART) framework, which allows for flexible, nonparametric regression, and uncertainty quantification while addressing the nonlinearity and nonstationarity inherent in shot selection patterns to provide a more accurate representation of the factors driving player performance;we further propose the Adaptive Functional Bayesian Additive Regression Trees (AFBART) model, which builds on FBART by introducing adaptive basis functions for improved computational efficiency and model fit. AFBART is particularly well suited for the analysis of two-dimensional shot intensity surfaces and provides a robust tool for uncovering latent patterns in shooting behavior. Through simulation studies and real-world applications to NBA player data, we demonstrate the effectiveness of the model in quantifying shooting tendencies, improving performance predictions, and informing strategic decisions for coaches, players, and team managers. This work represents a significant step forward in the statistical modeling of basketball shot selection and its applications in optimizing game strategies. Copyright © 2025, The Authors. All rights reserved.

关键词： Basketball

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Bayesian spatially Clustered Compositional Regression: Linking intersectoral GDP contributions to Gini Coefficients

arXiv

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arXiv 2024年

作者： Meng, Jingcheng Ren, Yimeng Zhu, Xuening Hu, Guanyu Department of Mathematics and Statistics Washington University in St Louis United States School of Data Science Fudan University China Center for Spatial Temporal Modeling for Applications in Population Sciences Department of Biostatistics and Data Science The University of Texas Health Science Center Houston United States

The Gini coefficient is an universally used measurement of income inequality. Intersectoral GDP contributions reveal the economic development of different sectors of the national economy. Linking intersectoral GDP contributions to Gini coefficients will provide better understandings of how the Gini coefficient is influenced by different industries. In this paper, a compositional regression with spatially clustered coefficients is proposed to explore heterogeneous effects over spatial locations under nonparametric Bayesian framework. Specifically, a Markov random field constraint mixture of finite mixtures prior is designed for Bayesian log contrast regression with compostional covariates, which allows for both spatially contiguous clusters and discontinous clusters. In addition, an efficient Markov chain Monte Carlo algorithm for posterior sampling that enables simultaneous inference on both cluster configurations and cluster-wise parameters is designed. The compelling empirical performance of the proposed method is demonstrated via extensive simulation studies and an application to 51 states of United States from 2019 Bureau of Economic Analysis. © 2024, CC BY-NC-SA.

关键词： Inference engines

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A Bayesian Circadian Hidden Markov Model to Infer Rest-Activity Rhythms Using 24-hour Actigraphy Data

arXiv

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arXiv 2023年

作者： Lu, Jiachen Xiao, Qian Bauer, Cici Department of Biostatistics and Data Science The University of Texas Health Science Center Houston School of Public Health United States Department of Epidemiology Human Genetics and Environmental Sciences The University of Texas Health Science Center Houston School of Public Health United States Center for Spatial-temporal Modeling for Applications in Population Sciences The University of Texas Health Science Center Houston School of Public Health United States

24-hour actigraphy data collected by wearable devices offer valuable insights into physical activity types, intensity levels, and rest-activity rhythms (RAR). RARs, or patterns of rest and activity exhibited over a 24-hour period, are regulated by the body’s circadian system, synchronizing physiological processes with external cues like the light-dark cycle. Disruptions to these rhythms, such as irregular sleep patterns, daytime drowsiness or shift work, have been linked to adverse health outcomes including metabolic disorders, cardiovascular disease, depression, and even cancer, making RARs a critical area of health research. In this study, we propose a Bayesian Circadian Hidden Markov Model (BCHMM) that explicitly incorporates 24-hour circadian oscillators mirroring human biological rhythms. The model assumes that observed activity counts are conditional on hidden activity states through Gaussian emission densities, with transition probabilities modeled by state-specific sinusoidal functions. Our comprehensive simulation study reveals that BCHMM outperforms frequentist approaches in identifying the underlying hidden states, particularly when the activity states are difficult to separate. BCHMM also excels with smaller Kullback-Leibler divergence on estimated densities. With the Bayesian framework, we address the label-switching problem inherent to hidden Markov models via a positive constraint on mean parameters. From the proposed BCHMM, we can infer the 24-hour rest-activity profile via time-varying state probabilities, to characterize the person-level RAR. We demonstrate the utility of the proposed BCHMM using 2011-2014 National Health and Nutrition Examination Survey (NHANES) data, where worsened RAR, indicated by lower probabilities in low-activity state during the day and higher probabilities in high-activity state at night, is associated with an increased risk of diabetes. © 2023, CC BY.

关键词： Hidden Markov models

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Bayesian Nonparametric Clustering with Feature Selection for spatially Resolved Transcriptomics Data

arXiv

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arXiv 2023年

作者： Zhu, Bencong Hu, Guanyu Xie, Yang Xu, Lin Fan, Xiaodan Li, Qiwei Department of Statistics The Chinese University of Hong Kong Hong Kong Department of Biostatistics and Data Science Center for Spatial Temporal Modeling for Applications in Population Sciences The University of Texas Health Science Center Houston United States School of Public Health The University of Texas Southwestern Medical Center United States Department of Statistics The Chinese University of Hong Kong Hong Kong Department of Mathematical Sciences The University of Texas Dallas United States

The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profiling while preserving spatial and morphological context. Nevertheless, there are inherent challenges associated with these new high-dimensional spatial data, such as zero-inflation, over-dispersion, and heterogeneity. These challenges pose obstacles to effective clustering, which is a fundamental problem in SRT data analysis. Current computational approaches often rely on heuristic data preprocessing and arbitrary cluster number prespecification, leading to considerable information loss and consequently, suboptimal downstream analysis. In response to these challenges, we introduce BNPSpace, a novel Bayesian nonparametric spatial clustering framework that directly models SRT count data. BNPSpace facilitates the partitioning of the whole spatial domain, which is characterized by substantial heterogeneity, into homogeneous spatial domains with similar molecular characteristics while identifying a parsimonious set of discriminating genes among different spatial domains. Moreover, BNPSpace incorporates spatial information through a Markov random field prior model, encouraging a smooth and biologically meaningful partition pattern. We assess the performance of BNPSpace utilizing both simulated and real SRT data. In the application to the human dorsolateral prefrontal cortex (DLPFC) 10x Visium data, comprising 4, 788 spots and 33, 538 genes with a 95% sparsity, BNPSpace outperforms existing methods by identifying more coherent spatial domain patterns and achieving the best clustering performance. Furthermore, the discriminating genes identified by BNPSpace showed significant enrichment with odd ratio = 1.877 (p-value = 0.00162) in DLPFC gene sets validated by real biological experiments, underscoring its effectiveness in revealing biologically relevant insights. Copyright © 2023, The Authors. All ri

关键词： Gene expression

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Accelerometer-measured chronoactivity and type 2 diabetes risk: A prospective study in UK Biobank participants

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Preventive Medicine 2025年 197卷 108312页

作者： Feng, Qiuyu Albalak, Gali van Dijk, Ko Willems van der Velde, Jeroen H.P.M. Xiao, Qian Noordam, Raymond van Heemst, Diana Department of Human Genetics Leiden University Medical Center Leiden Netherlands Section of Gerontology and Geriatrics Department of Internal Medicine Leiden University Medical Center Leiden Netherlands Division of Endocrinology Department of Internal Medicine Leiden University Medical Center Leiden Netherlands Einthoven Laboratory for Experimental Vascular Medicine Leiden University Medical Center Leiden Netherlands Department of Clinical Epidemiology Leiden University Medical Center Leiden Netherlands Department of Epidemiology Human Genetics and Environmental Sciences School of Public Health University of Texas Health Science Center at Houston Houston TX United States Center for Spatial-Temporal Modeling for Applications in Population Sciences School of Public Health University of Texas Health Science Center at Houston Houston TX United States Health Campus the Hague/Public Health and Primary Care Leiden University Medical Center The Hague Netherlands

Objective: We assessed whether timing of physical activity, independent from the total activity amount, − which we refer to as chronoactivity – is associated with type 2 diabetes (T2D) risk. Methods: We included UK Biobank participants with valid accelerometry data (UK, exposure measurement: 2013–2015, follow-up till November 2023) and without diabetes mellitus at baseline (N = 89,439;mean age: 61.7 [SD:7.9] years). Relative hourly physical activity was calculated by dividing the average hourly clock time physical activity by the average hourly physical activity in a week. Participants were categorized into different chronoactivity clusters using k-means cluster analysis on relative hourly physical activity. We used multivariable-adjusted cox-proportional hazard regressions to examine associations between relative hourly physical activity, chronoactivity clusters and T2D, adjusted for potential confounders, including BMI as a potential mediator. Results: Over 7.8 (interquartile range: 7.2 to 8.3) years of follow-up, 2240 participants developed T2D. Higher relative hourly activity amounts during late morning (8:00–10:59) and late afternoon (15:00–15:59, 17:00–17:59) were associated with approximately 5 %–10 % lower T2D risk. Four clusters of chronoactivity patterns were identified, notably: midday (reference), early morning peak, late morning peak, and evening peak. Compared with participants exhibiting a midday pattern, those with a late morning peak had a lower T2D risk (Hazards Ratio: 0.88, 95 %CI: 0.79, 0.98). Overall, all observations attenuated after additional BMI adjustment. Conclusions: Independent of the total amount of physical activity, specific timing of physical activity represents an additional dimension in T2D risk. © 2025 The Authors

关键词： Cohort study Lifestyle factor Physical activity Triaxial accelerometer Type 2 diabetes

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Examining Demographic, Geographic, and temporal Patterns of Melanoma Incidence in Texas From 2000 to 2018: Retrospective Study

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JMIR Cancer 2025年 11卷 e67902页

作者： Zhang, Kehe Taylor, Madison M. Hunyadi, Jocelyn Doan, Hung Q. Adamson, Adewole S. Miller, Paige Nelson, Kelly C. Bauer, Cici Department of Biostatistics and Data Science School of Public Health The University of Texas Health Science Center at Houston Houston TX United States Center for Spatial-Temporal Modeling for Applications in Population Sciences School of Public Health The University of Texas Health Science Center at Houston Houston TX United States John P. and Kathrine G. McGovern Medical School The University of Texas Health Science Center at Houston Houston TX United States Department of Dermatology Division of Internal Medicine The University of Texas MD Anderson Cancer Center Houston TX United States Division of Dermatology Dell Medical School The University of Texas at Austin Austin TX United States Cancer Epidemiology and Surveillance Branch Texas Department of State Health Services Austin TX United States

Background: Melanoma currently ranks as the fifth leading cancer diagnosis and is projected to become the second most common cancer in the United States by 2040. Melanoma detected at earlier stages may be treated with less-risky and less-costly therapeutic options. Objective: This study aims to analyze temporal and spatial trends in melanoma incidence by stage at diagnosis (overall, early, and late) in Texas from 2000 to 2018, focusing on demographic and geographic variations to identify high-risk populations and regions for targeted prevention efforts. Methods: We used melanoma incidence data from all 254 Texas counties from the Texas Cancer Registry (TCR) from 2000 to 2018, aggregated by county and year. Among these, 250 counties reported melanoma cases during the period. Counties with no cases reported in a certain year were treated as having no cases. Melanoma cases were classified by SEER Summary Stage and stratified by the following four key covariates: age, sex, race and ethnicity, and stage at diagnosis. Incidence rates (IRs) were calculated per 100,000 population, and temporal trends were analyzed using joinpoint regression to determine average annual percentage changes (AAPCs) with 95% CIs for the whole time period (2000‐2018), the most recent 10-year period (2009‐2018), and the most recent 5-year period (2014‐2018). Heat map visualizations were developed to assess temporal trends by patient age, year of diagnosis, stage at diagnosis, sex, and race and ethnicity. spatial cluster analysis was conducted using Getis-Ord Gi* statistics to identify county-level geographic clusters of high and low melanoma incidence by stage at diagnosis. Results: A total of 82,462 melanoma cases were recorded, of which 74.7% (n=61,588) were early stage, 11.3% (n=9,352) were late stage, and 14% (n=11,522) were of unknown stage. Most cases were identified as males and non-Hispanic White individuals. Melanoma IRs increased from 2000 to 2018, particularly among older adults (60+ ye

关键词： demographic variation geographic disparity geospatial analysis joinpoint regression melanoma incidence melanoma screening stage at diagnosis temporal trend analysis

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Review of the Study of Nonlinear Atmospheric Dynamics in China (1999-2002)

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Advances in Atmospheric sciences 2004年第3期21卷 399-406页

作者：刁一娜封国林刘式达刘式适罗德海黄思训陆维松丑纪范 Department of Atmospheric and Oceanic Sciences Ocean University of China Qingdao 266003 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid DynamicsInstitute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 Department of Atmospheric Sciences Peking University Beijing 100871 Department of Atmospheric and Oceanic Sciences Ocean University of China Qingdao 266003 P. L. A. University of Science & Engineering Meteorological Institute Nanjing 211101 Nanjing Institute of Meteorology Nanjing 210044 Beijing Training Center China Meteorological Administration Beijing 100081esearches on nonlinear atmospheric dynamics in China (1999-2002) are briefly surveyed. This review includes the major achievements in the following branches of nonlinear dynamics: nonlinear stability theory nonlinear blocking dynamics 3D spiral structure in the atmosphere traveling wave solution of the nonlinear evolution equation numerical predictability in a chaotic system and global analysis of climate dynamics. Some applications of nonlinear methods such as hierarchy structure of climate and scaling invariance the spatial-temporal series predictive method the nonlinear inverse problem and a new difference scheme with multi-time Ievels are also introduced.

Researches on nonlinear atmospheric dynamics in China (1999-2002) are briefly surveyed. This review includes the major achievements in the following branches of nonlinear dynamics: nonlinear stability theory, nonlinear blocking dynamics, 3D spiral structure in the atmosphere, traveling wave solution of the nonlinear evolution equation, numerical predictability in a chaotic system, and global analysis of climate dynamics. Some applications of nonlinear methods such as hierarchy structure of climate and scaling invariance, the spatial-temporal series predictive method, the nonlinear inverse problem, and a new difference scheme with multi-time Ievels are also introduced.

关键词： nonlinear dynamics stability blocking spiral structure traveling wave global analysis

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