Statistics - 2023

Sninsky, J. A., Staicu, A.-M., & Barnes, E. L. (2023, September 21). In Acute Severe Ulcerative Colitis Patients Who Receive Rescue Therapy, Prior Maintenance Therapy and Day 3 C-Reactive Protein After Rescue Therapy Are Associated With 12-Month Colectomy Risk. INFLAMMATORY BOWEL DISEASES. https://doi.org/10.1093/ibd/izad215

Zhou, W., & Lahiri, S. (2023, August 25). Stationary Jackknife. JOURNAL OF TIME SERIES ANALYSIS. https://doi.org/10.1111/jtsa.12714

Somers, T. J. J., Winger, J. G. G., Fisher, H. M. M., Hyland, K. A. A., Davidian, M., Laber, E. B. B., … Keefe, F. J. J. (2023). Behavioral cancer pain intervention dosing: results of a Sequential Multiple Assignment Randomized Trial. PAIN, 164(9), 1935–1941. https://doi.org/10.1097/j.pain.0000000000002915

Li, R., & Xiao, L. (2023, September 4). Latent factor model for multivariate functional data. BIOMETRICS, Vol. 9. https://doi.org/10.1111/biom.13924

Larsen, N., Stallrich, J., Sengupta, S., Deng, A., Kohavi, R., & Stevens, N. T. (2023). Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology. The American Statistician. https://doi.org/10.1080/00031305.2023.2257237

Cordova, A. C., Dodds, J. N., Tsai, H.-H. D., Lloyd, D. T., Roman-Hubers, A. T., Wright, F. A., … Rusyn, I. (2023, August 22). Application of Ion Mobility Spectrometry-Mass Spectrometry for Compositional Characterization and Fingerprinting of a Library of Diverse Crude Oil Samples. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. https://doi.org/10.1002/etc.5727

Ji, X., Fisher, A. A., Su, S., Thorne, J. L., Potter, B., Lemey, P., … Suchard, M. A. (2023, July 17). Scalable Bayesian Divergence Time Estimation With Ratio Transformations. SYSTEMATIC BIOLOGY. https://doi.org/10.1093/sysbio/syad039

Sakolish, C., Moyer, H. L., Tsai, H.-H. D., Ford, L. C., Dickey, A. N., Wright, F. A., … Rusyn, I. (2023). Analysis of reproducibility and robustness of a renal proximal tubule microphysiological system OrganoPlate 3-lane 40 for in vitro studies of drug transport and toxicity. TOXICOLOGICAL SCIENCES. https://doi.org/10.1093/toxsci/kfad080

Yang, H., Ruiz-Suarez, S., Reich, B., Guan, Y., & Rappold, A. (2023). A Data-Fusion Approach to Assessing the Contribution of Wildland Fire Smoke to Fine Particulate Matter in California. Remote Sensing. https://doi.org/10.3390/rs15174246

Wagner, T., Schliep, E. M., North, J. S., Kundel, H., Custer, C. A., Ruzich, J. K., & Hansen, G. J. A. (2023). Predicting climate change impacts on poikilotherms using physiologically guided species abundance models. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 120(15). https://doi.org/10.1073/pnas.2214199120

Clark, K., Fu, W., Liu, C.-L., Ho, P.-C., Wang, H., Lee, W.-P., … Tzeng, J.-Y. (2023). The prediction of Alzheimer's disease through multi-trait genetic modeling. FRONTIERS IN AGING NEUROSCIENCE, 15. https://doi.org/10.3389/fnagi.2023.1168638

Zahan, N., Shohan, S., Harris, D., & Williams, L. (2023). Do Software Security Practices Yield Fewer Vulnerabilities? 2023 IEEE/ACM 45TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: SOFTWARE ENGINEERING IN PRACTICE, ICSE-SEIP, pp. 292–303. https://doi.org/10.1109/ICSE-SEIP58684.2023.00032

Koner, S., & Williams, J. P. (2023). The EAS approach to variable selection for multivariate response data in high-dimensional settings. ELECTRONIC JOURNAL OF STATISTICS, 17(2), 1947–1995. https://doi.org/10.1214/23-EJS2141

Werthmann, D., Joode, B. van W., Cuffney, M. T., Reich, B. J., Soto-Martinez, M. E., Corrales-Vargas, A., … Hoppin, J. A. (2023). A cross-sectional analysis of medical conditions and environmental factors associated with fractional exhaled nitric oxide (FeNO) in women and children from the ISA birth cohort, Costa Rica. ENVIRONMENTAL RESEARCH, 233. https://doi.org/10.1016/j.envres.2023.116449

Ray, M., Guha, S., Dhungana, R. R., Karak, A., Choudhury, B., Ray, B., … Selker, H. P. (2023). Development and validation of a predictive model for the diagnosis of rheumatic heart disease in low-income countries based on two cross-sectional studies. INTERNATIONAL JOURNAL OF CARDIOLOGY CARDIOVASCULAR RISK AND PREVENTION, 18. https://doi.org/10.1016/j.ijcrp.2023.200195

Small, G. W., Akhtari, F. S., Green, A. J., Havener, T. M., Sikes, M., Quintanhila, J., … Wiltshire, T. (2023). Pharmacogenomic Analyses Implicate B Cell Developmental Status and MKL1 as Determinants of Sensitivity toward Anti-CD20 Monoclonal Antibody Therapy. CELLS, 12(12). https://doi.org/10.3390/cells12121574

Liu, Z., Clifton, J., Laber, E. B., Drake, J., & Fang, E. X. (2023, July 8). Deep Spatial Q-Learning for Infectious Disease Control. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS. https://doi.org/10.1007/s13253-023-00551-4

Awasthi, C., Archfield, S. A., Reich, B. J., & Sankarasubramanian, A. (2023). Beyond Simple Trend Tests: Detecting Significant Changes in Design-Flood Quantiles. GEOPHYSICAL RESEARCH LETTERS, 50(13). https://doi.org/10.1029/2023GL103438

Sahoo, I., Guinness, J., & Reich, B. J. J. (2023, July 6). Estimating atmospheric motion winds from satellite image data using space-time drift models. ENVIRONMETRICS, Vol. 7. https://doi.org/10.1002/env.2818

Weishampel, A., Staicu, A.-M., & Rand, W. (2023). Classification of social media users with generalized functional data analysis. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 179. https://doi.org/10.1016/j.csda.2022.107647

Cordova, A. C., Klaren, W. D., Ford, L. C., Grimm, F. A., Baker, E. S., Zhou, Y.-H., … Rusyn, I. (2023). Integrative Chemical-Biological Grouping of Complex High Production Volume Substances from Lower Olefin Manufacturing Streams. TOXICS, 11(7). https://doi.org/10.3390/toxics11070586

Gonzalez, R. D., Small, G. W., Green, A. J., Akhtari, F. S., Havener, T. M., Quintanilha, J. C. F., … Wiltshire, T. (2023). RYK Gene Expression Associated with Drug Response Variation of Temozolomide and Clinical Outcomes in Glioma Patients. PHARMACEUTICALS, 16(5). https://doi.org/10.3390/ph16050726

Li, M., Liu, W., Si, J., Stallrich, J. W., & Huang, H. (2023). Hierarchical Optimization for Control of Robotic Knee Prostheses Toward Improved Symmetry of Propulsive Impulse. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 70(5), 1634–1642. https://doi.org/10.1109/TBME.2022.3224026

Gao, C., & Yang, S. (2023). Pretest estimation in combining probability and non-probability samples. ELECTRONIC JOURNAL OF STATISTICS, 17(1), 1492–1546. https://doi.org/10.1214/23-EJS2137

Tabaie, A., Sengupta, S., Pruitt, Z. M., & Fong, A. (2023). A natural language processing approach to categorise contributing factors from patient safety event reports. BMJ HEALTH & CARE INFORMATICS, 30(1). https://doi.org/10.1136/bmjhci-2022-100731

Majumder, R., & Reich, B. J. (2023). A deep learning synthetic likelihood approximation of a non-stationary spatial model for extreme streamflow forecasting. SPATIAL STATISTICS, 55. https://doi.org/10.1016/j.spasta.2023.100755

Gonzalez, R. D., Small, G. W., Green, A. J., Akhtari, F. S., Motsinger-Reif, A. A., Quintanilha, J. C. F., … Wiltshire, T. (2023). MKX-AS1 Gene Expression Associated with Variation in Drug Response to Oxaliplatin and Clinical Outcomes in Colorectal Cancer Patients. PHARMACEUTICALS, 16(5). https://doi.org/10.3390/ph16050757

Leshchev, D., J. S. Valentine, A., Kim, P., Mills, A. W., Roy, S., Chakraborty, A., … Chen, L. X. X. (2023, May 31). Revealing Excited-State Trajectories on Potential Energy Surfaces with Atomic Resolution in Real Time. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION. https://doi.org/10.1002/anie.202304615

Kline, E., & Sengupta, S. (2023). How AI can Help us Understand and Mitigate Error Propagation in Radiation Oncology. In Artificial Intelligence in Radiation Oncology (pp. 305–334). https://doi.org/10.1142/9789811263545_0014

Komolafe, T., Fong, A., & Sengupta, S. (2023). Scalable Community Extraction of Text Networks for Automated Grouping in Medical Databases. Journal of Data Science. https://doi.org/10.6339/22-JDS1038

Yanchenko, E., & Sengupta, S. (2023). Core-periphery structure in networks: A statistical exposition. Statistics Surveys, 17(none), 42–74. https://doi.org/10.1214/23-SS141

Dixit, V., & Martin, R. (2023). A PRticle filter algorithm for nonparametric estimation of multivariate mixing distributions. STATISTICS AND COMPUTING, 33(4). https://doi.org/10.1007/s11222-023-10242-2

Ting, B. W. W., Wright, F. A., & Zhou, Y.-H. (2023, May 22). Simultaneous modeling of multivariate heterogeneous responses and heteroskedasticity via a two-stage composite likelihood. BIOMETRICAL JOURNAL. https://doi.org/10.1002/bimj.202200029

Tsai, H.-H. D., House, J. S., Wright, F. A., Chiu, W. A., & Rusyn, I. (2023, April 20). A tiered testing strategy based on in vitro phenotypic and transcriptomic data for selecting representative petroleum UVCBs for toxicity evaluation in vivo. TOXICOLOGICAL SCIENCES. https://doi.org/10.1093/toxsci/kfad041

Chu, J., Lu, W., & Yang, S. (2023, March 15). Targeted optimal treatment regime learning using summary statistics. BIOMETRIKA, Vol. 3. https://doi.org/10.1093/biomet/asad020

Zhou, Y.-H., Gallins, P. J., Pace, R. G., Dang, H., Aksit, M. A., Blue, E. E., … Knowles, M. R. (2023). Genetic Modifiers of Cystic Fibrosis Lung Disease Severity. AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 207(10), 1324–1333. https://doi.org/10.1164/rccm.202209-1653OC

Gao, Y., Shi, C., & Song, R. (2023). Deep spectral Q-learning with application to mobile health. STAT, 12(1). https://doi.org/10.1002/sta4.564

Wang, K., & Ghosal, S. (2023). Posterior contraction and testing for multivariate isotonic regression. ELECTRONIC JOURNAL OF STATISTICS, 17(1), 798–822. https://doi.org/10.1214/23-EJS2115

Ganguly, I., Buhrman, G., Kline, E., Mun, S. K. K., & Sengupta, S. (2023). Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing. DIAGNOSTICS, 13(7). https://doi.org/10.3390/diagnostics13071215

Hector, E. C., & Reich, B. J. (2023, April 12). Distributed Inference for Spatial Extremes Modeling in High Dimensions. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 4. https://doi.org/10.1080/01621459.2023.2186886

Stallrich, J., Allen-Moyer, K., & Jones, B. (2023, April 7). D- and A-Optimal Screening Designs. TECHNOMETRICS. https://doi.org/10.1080/00401706.2023.2183262

Chen, H., Lu, W., Song, R., & Ghosh, P. (2023, April 12). On Learning and Testing of Counterfactual Fairness through Data Preprocessing. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 4. https://doi.org/10.1080/01621459.2023.2186885

Koner, S., & Staicu, A.-M. (2023). Second-Generation Functional Data. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, 10, 547–572. https://doi.org/10.1146/annurev-statistics-032921-033726

Gao, C., Yang, S., & Kim, J. K. (2023, March 2). Soft calibration for selection bias problems under mixed-effects models. BIOMETRIKA, Vol. 3. https://doi.org/10.1093/biomet/asad016

Yang, S., Gao, C., Zeng, D., & Wang, X. (2023, April 6). Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, Vol. 4. https://doi.org/10.1093/jrsssb/qkad017

Luo, L., Wang, J., & Hector, E. C. (2023, February 20). Statistical inference for streamed longitudinal data. BIOMETRIKA. https://doi.org/10.1093/biomet/asad010

Holter, J. C., & Stallrich, J. W. (2023). Tuning parameter selection for penalized estimation via R2. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 183. https://doi.org/10.1016/j.csda.2023.107729

Heng, Q., Chi, E. C., & Liu, Y. (2023). Robust Low-rank Tensor Decomposition with the L2 Criterion. Technometrics. https://doi.org/10.1080/00401706.2023.2200541

Pragya, A., & Ghosh, T. K. (2023). Soft Functionally Gradient Materials and Structures – Natural and Manmade: A Review. Advanced Materials. https://doi.org/10.1002/adma.202300912

Manschot, C., Laber, E., & Davidian, M. (2023, March 22). Interim monitoring of sequential multiple assignment randomized trials using partial information. BIOMETRICS, Vol. 3. https://doi.org/10.1111/biom.13854

Syring, N., & Martin, R. (2023). Gibbs posterior concentration rates under sub-exponential type losses. BERNOULLI, 29(2), 1080–1108. https://doi.org/10.3150/22-BEJ1491

Long, A. S., Reich, B. J., Staicu, A.-M., & Meitzen, J. (2023, March 13). A nonparametric test of group distributional differences for hierarchically clustered functional data. BIOMETRICS, Vol. 3. https://doi.org/10.1111/biom.13846

Zou, H., Xiao, L., Zeng, D., & Luo, S. (2023, February 20). Multivariate functional mixed model with MRI data: An application to Alzheimer's disease. STATISTICS IN MEDICINE, Vol. 2. https://doi.org/10.1002/sim.9683

Lu, X., Che, Y., Rejesus, R. M., Goodwin, B. K., Ghosh, S. K., & Paudel, J. (2023). Unintended environmental benefits of crop insurance: Nitrogen and phosphorus in water bodies. ECOLOGICAL ECONOMICS, 204. https://doi.org/10.1016/j.ecolecon.2022.107657

Williams, J. P., Ommen, D. M., & Hannig, J. (2023). GENERALIZED FIDUCIAL FACTOR: AN ALTERNATIVE TO THE BAYES FACTOR FOR FORENSIC IDENTIFICATION OF SOURCE PROBLEMS. ANNALS OF APPLIED STATISTICS, 17(1), 378–402. https://doi.org/10.1214/22-AOAS1632

Shi, L., Wank, M., Chen, Y., Wang, Y., Liu, Y., Hector, E. C., & Song, P. X. K. (2023). Sleep Classification With Artificial Synthetic Imaging Data Using Convolutional Neural Networks. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 27(1), 421–432. https://doi.org/10.1109/JBHI.2022.3210485

Kirchner, M., Miller, W. G., Osborne, J. A., Badgley, B., Neidermeyer, J., & Kathariou, S. (2023). Campylobacter Colonization and Diversity in Young Turkeys in the Context of Gastrointestinal Distress and Antimicrobial Treatment. MICROORGANISMS, 11(2). https://doi.org/10.3390/microorganisms11020252

Deng, Y., & Gao, C. (2023, February 9). Where does the risk lie? Systemic risk and tail risk networks in the Chinese financial market. PACIFIC ECONOMIC REVIEW, Vol. 2. https://doi.org/10.1111/1468-0106.12417

Wu, P.-S., & Martin, R. (2023). A Comparison of Learning Rate Selection Methods in Generalized Bayesian Inference. BAYESIAN ANALYSIS, 18(1), 105–132. https://doi.org/10.1214/21-BA1302

Sanders, C. W., Stewart, D. L., Pacifici, K., Hess, G. R., Olfenbuttel, C., & DePerno, C. S. (2023, January 23). Variations in reproduction and age structure in the North American river otter in North Carolina, USA. JOURNAL OF WILDLIFE MANAGEMENT. https://doi.org/10.1002/jwmg.22361

Wang, Y., Tzeng, J.-Y., Huang, Y., Maguire, R., Hoyo, C., & Allen, T. K. (2023). Duration of exposure to epidural anesthesia at delivery, DNA methylation in umbilical cord blood and their association with offspring asthma in Non-Hispanic Black women. ENVIRONMENTAL EPIGENETICS, 9(1). https://doi.org/10.1093/eep/dvac026

Song, K., & Zhou, Y.-H. (2023). Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations. BIOENGINEERING-BASEL, 10(2). https://doi.org/10.3390/bioengineering10020231

Heng, Q., Zhou, H., & Chi, E. C. (2023, February 24). Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol. 2. https://doi.org/10.1080/10618600.2023.2170089

Lee, D., & Ghosh, S. (2023). Bayesian Analysis of First-Order Markov Models for Autocorrelated Binary Responses. JOURNAL OF STATISTICAL THEORY AND PRACTICE, 17(1). https://doi.org/10.1007/s42519-022-00305-4

Shao, S., Canner, J. E. E., Everett, R. A., Bekele-Maxwell, K., Kuerbis, A., Stephenson, L., … Banks, H. T. (2023). A Comparison of Mathematical and Statistical Modeling with Longitudinal Data: An Application to Ecological Momentary Assessment of Behavior Change in Individuals with Alcohol Use Disorder. BULLETIN OF MATHEMATICAL BIOLOGY, 85(1). https://doi.org/10.1007/s11538-022-01097-1

Wang, Y., & Ghosh, S. K. (2023). Nonparametric estimation of isotropic covariance function. Journal of Nonparametric Statistics. https://doi.org/10.1080/10485252.2022.2146111

Liu, X., Chi, E. C., & Lange, K. (2023). A Sharper Computational Tool for Regression. Technometrics. https://doi.org/10.1080/00401706.2022.2118172

Hector, E. C., Luo, L., & Song, P. X.-K. (2023). Parallel-and-stream accelerator for computationally fast supervised learning. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 177. https://doi.org/10.1016/j.csda.2022.107587

Ghosal, R., Ghosh, S., Urbanek, J., Schrack, J. A., & Zipunnikov, V. (2023). Shape-constrained estimation in functional regression with Bernstein polynomials. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 178. https://doi.org/10.1016/j.csda.2022.107614

Roberts, E., Ghosh, S., & Pourdeyhimi, B. (2023). Process–Structure–Property relationship of roping in meltblown nonwovens. The Journal of The Textile Institute. https://doi.org/10.1080/00405000.2022.2029277