{"id":9,"date":"2021-08-06T14:03:11","date_gmt":"2021-08-06T18:03:11","guid":{"rendered":"https:\/\/sciences.ucf.edu\/statistics\/aistats\/?page_id=9"},"modified":"2024-07-02T16:32:01","modified_gmt":"2024-07-02T20:32:01","slug":"publications","status":"publish","type":"page","link":"https:\/\/sciences.ucf.edu\/statistics\/aistats\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"
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Preprint<\/i><\/h3>\n
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W. Fan, S. Zheng, P. Wang, R. Xie, J. Bian, and Y. Fu, \u201cAddressing Distribution Shift in Time Series Forecasting with Instance Normalization Flows,\u201d arXiv preprint arXiv:2401.16777<\/i>, 2024.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
X. Wang, D. Wang, W. Ying, R. Xie, H. Chen, and Y. Fu, \u201cKnockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent,\u201d arXiv preprint arXiv:2403.04015<\/i>, 2024.<\/div>\n<\/div>\n
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<\/div>\n<\/div>\n

2024<\/h3>\n
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\u00a0R. Choudhury et al.<\/i>, \u201cAssociations between monitor-independent movement summary (MIMS) and fall risk appraisal combining fear of falling and physiological fall risk in community-dwelling older adults,\u201d Frontiers in aging<\/i>, vol. 5, p. 1284694, 2024.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
Wheeler, \u201cA mixed methods approach to understand mother-father relationship and perceived stress among Black pregnant women,\u201d Journal of Perinatal and Neonatal Nursing<\/i>, 2024.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
K. Lafontant et al.<\/i>, \u201cComparing Sensitivity, Specificity, and Accuracy of Fall Risk Assessments in Community-Dwelling Older Adults,\u201d Clinical Interventions in Aging<\/i>, pp. 581\u2013588, 2024.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
J. Crook, E. Lee, R. Xie, O. Bolajoko, and V. Loerzel, \u201cNutritional mediators of inflammation: A cause for increased surveillance,\u201d Cancer Research<\/i>, vol. 84, no. 6_Supplement, pp. 2171\u20132171, 2024.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
J. M. Crook, E. Lee, O. Bolajoko, and R. Xie, \u201cPoverty, food insecurity, and nutritional inflammatory mediators of allostatic load,\u201d Cancer Research<\/i>, vol. 84, no. 6_Supplement, pp. 2198\u20132198, 2024.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
T. Nguyen, L. Thiamwong, Q. Lou, and R. Xie, \u201cUnveiling Fall Triggers in Older Adults: A Machine Learning Graphical Model Analysis,\u201d Mathematics<\/i>, vol. 12, no. 9, p. 1271, 2024.<\/div>\n<\/div>\n<\/div>\n

2023<\/h3>\n
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R. Choudhury, J.-H. Park, C. Banarjee, L. Thiamwong, R. Xie, and J. R. Stout, \u201cAssociations of Mutually Exclusive Categories of Physical Activity and Sedentary Behavior with Body Composition and Fall Risk in Older Women: A Cross-Sectional Study,\u201d International Journal of Environmental Research and Public Health<\/i>, vol. 20, no. 4, p. 3595, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
R. Xie, S. Bai, and P. Ma, \u201cOptimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data,\u201d Annals of Applied Statistics<\/i>, vol. 17, no. 4, pp. 3195\u20133215, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
L. Thiamwong et al.<\/i>, \u201cCultural Adaptation and Pilot Testing of the CDC\u2019s Stopping Elderly Accidents, Deaths, and Injuries (STEADI) Program for Older Adults in a Low and Middle-Income Country,\u201d 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
L. Thiamwong, R. Xie, N. Lighthall, J.-H. Park, V. Loerzel, and J. Stout, \u201cFALL RISK, FEAR OF FALLING, PHYSICAL ACTIVITY, AND SELF-PERCEPTIONS IN ETHNICALLY DIVERSE LOW-INCOME OLDER ADULTS,\u201d in INNOVATION IN AGING<\/i>, 2022, vol. 6, pp. 106\u2013106.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
L. Thiamwong et al.<\/i>, \u201cLevels of Accelerometer-Based Physical Activity in Older Adults With a Mismatch Between Physiological Fall Risk and Fear of Falling,\u201d Journal of Gerontological Nursing<\/i>, vol. 49, no. 6, pp. 41\u201349, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
L. Thiamwong, R. Xie, N. E. Conner, J. M. Renziehausen, E. O. Ojo, and J. R. Stout, \u201cBody composition, fear of falling and balance performance in community-dwelling older adults,\u201d Translational Medicine of Aging<\/i>, vol. 7, pp. 80\u201386, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
L. Thiamwong et al.<\/i>, \u201cOptimizing a Technology-Based Body and Mind Intervention to Prevent Falls and Reduce Health Disparities in Low-Income Populations: Protocol for a Clustered Randomized Controlled Trial,\u201d JMIR Research Protocols<\/i>, vol. 12, no. 1, p. e51899, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
Malatyali, \u201cHealth Disparities in Cognitive Impairment and Dementia: Role of Social Strain, Depression, and C-Reactive Protein,\u201d Gerontology and Geriatric Medicine<\/i>, vol. 9, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
K. Rathbun et al.<\/i>, \u201c1153: ORAL MICROBIOME CHANGES OVER TIME IN HOSPITALIZED OLDER ADULTS FROM SKILLED NURSING FACILITY VS HOME,\u201d Critical Care Medicine<\/i>, vol. 52, no. 1, p. S548, 2024.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
A. Malatyali, T. Cidav, R. Xie, and L. Thiamwong, \u201cVIGOROUS PHYSICAL ACTIVITY, DEPRESSION, PRO-INFLAMMATORY CYTOKINE AND RACIAL\/ETHNIC VARIATIONS IN FALLS,\u201d Innovation in Aging<\/i>, vol. 7, no. Suppl 1, p. 606, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
R. Choudhury et al.<\/i>, \u201cACCELEROMETRY-BASED PHYSICAL ACTIVITY (MIMS\/DAY) AND FALL RISK APPRAISAL MATRIX IN OLDER ADULTS,\u201d Innovation in Aging<\/i>, vol. 7, no. Suppl 1, p. 700, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
A. Blount et al.<\/i>, \u201cINVESTIGATING PSYCHOLOGICAL FACTORS AS A PREDICTOR OF FEAR OF FALLING AND FALL RISK IN OLDER ADULTS,\u201d Innovation in Aging<\/i>, vol. 7, no. Supplement_1, pp. 794\u2013794, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
A. Malatyali, T. Cidav, R. Xie, and L. Thiamwong, \u201cINFLAMMATORY CYTOKINE, SOCIAL SUPPORT, AND HEALTH DISPARITIES IN PERSONS WITH COGNITIVE IMPAIRMENT AND DEMENTIA,\u201d Innovation in Aging<\/i>, vol. 7, no. Suppl 1, p. 854, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
E. O. Ojo et al.<\/i>, \u201cCONTROL BELIEFS, AND ATTITUDINAL BELIEFS OF USING TECHNOLOGY IN LOW-INCOME OLDER ADULTS,\u201d in INNOVATION IN AGING<\/i>, 2023, vol. 7, pp. 678\u2013679.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
C. Banarjee, R. Choudhury, J.-H. Park, R. Xie, J. Stout, and L. Thiamwong, \u201cDECLINE IN OBJECTIVELY MEASURED STATIC BALANCE WITH AGING,\u201d Innovation in Aging<\/i>, vol. 7, no. Supplement_1, pp. 910\u2013911, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
K. Lafontant, L. Thiamwong, J. Stout, J.-H. Park, R. Xie, and D. Fukuda, \u201cREDEFINING OBESITY: A RATIO OF FAT AND MUSCLE MASS COMPARED TO BODY MASS INDEX IN OLDER ADULTS,\u201d Innovation in Aging<\/i>, vol. 7, no. Suppl 1, p. 1109, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
R. Choudhury, J.-H. Park, C. Banarjee, L. Thiamwong, R. Xie, and J. Stout, \u201cBODY COMPOSITION AND FALL RISK IN MUTUALLY EXCLUSIVE CATEGORIES OF PHYSICAL ACTIVITY IN OLDER WOMEN,\u201d Innovation in Aging<\/i>, vol. 7, no. Supplement_1, pp. 597\u2013597, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
R. Xie, L. Chen, J.-H. Park, J. Stout, and L. Thiamwong, \u201cSMOOTHING SPLINE ANOVA MONITORING PHYSICAL ACTIVITY AND FALL RISK IN COMMUNITY-DWELLING OLDER ADULTS,\u201d Innovation in Aging<\/i>, vol. 7, no. Suppl 1, p. 876, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
L. Thiamwong et al.<\/i>, \u201cURBANIZATION, FALLS, AND FEAR OF FALLING IN COMMUNITY-DWELLING OLDER ADULTS IN THAILAND,\u201d Innovation in Aging<\/i>, vol. 7, no. Supplement_1, pp. 239\u2013239, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
K. Lafontant, L. Thiamwong, J. Stout, J.-H. Park, R. Xie, and D. Fukuda, \u201cEXAMINING THE ASSOCIATION BETWEEN HYDRATION STATUS AND OBESITY IN OLDER ADULTS,\u201d Innovation in Aging<\/i>, vol. 7, no. Suppl 1, p. 1032, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
L. Thiamwong et al.<\/i>, \u201cBRINGING COMMUNITY-BASED FALLS PREVENTION PROGRAM TO IMPROVE GLOBAL HEALTH (BRIGHT),\u201d Innovation in Aging<\/i>, vol. 7, no. Suppl 1, p. 793, 2023.<\/div>\n
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\u00b7<\/div>\n
S. Hyer, S. Vaughan, J. W. Davis, R. Xie, D. Misra, and C. Giurgescu, \u201cThe Association of Avoidance Coping with Gestational Weight Gain among Pregnant Black Women,\u201d Western Journal of Nursing Research<\/i>, vol. 45, no. 3, pp. 226\u2013233, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
Y. Pan, L. Thiamwong, and R. Xie, \u201cThe Effects of Nurse Driven Mobility Intervention (NDMI) on Activities of Daily Living, Mobility, Fear of Falling, and Balance Performance in Hospitalized Older Patients: A pilot study,\u201d Geriatric Nursing<\/i>, vol. 49, pp. 193\u2013198, 2023.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
A. M. Bourgault, C. Deb, L. Aguirre, R. Xie, K. P. Rathbun, and M. L. Sole, \u201cMicrobiome profile informs cleansing and storage practices for reusable feeding tube stylets in critical care,\u201d Nutrition in Clinical Practice<\/i>, vol. 38, no. 2, pp. 411\u2013424, 2023.<\/div>\n<\/div>\n

2022<\/h3>\n
L. Thiamwong, R. Xie, J.-H. Park, N. Lighthall, V. Loerzel, and J. Stout, \u201cA TECHNOLOGY-BASED BODY-MIND INTERVENTION FOR LOW-INCOME AMERICAN OLDER ADULTS,\u201d Innovation in Aging<\/i>, vol. 6, no. Supplement_1, pp. 273\u2013273, 2022.<\/div>\n
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\u00b7<\/div>\n
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J. W. Davis et al.<\/i>, \u201cPhysical activity changes among non-Hispanic Black pregnant women,\u201d Public Health Nursing<\/i>, vol. 39, no. 4, pp. 744\u2013751, 2022.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
S. Hyer, W. Hu, M. Hu, J. W. Davis, R. Xie, and C. Giurgescu, \u201cRelationship with the Father of the Baby and Pregnancy-Related Anxiety among Pregnant Black Women,\u201d MCN: The American Journal of Maternal\/Child Nursing<\/i>, vol. 47, no. 4, pp. 213\u2013219, 2022.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
A. Bourgault et al.<\/i>, \u201cMicrobiome Analysis Informs Small-bore Feeding Tube Stylet Cleansing And Storage Practices,\u201d Critical Care Medicine<\/i>, vol. 50, no. 1, p. 286, 2022.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
W. Fan, K. Liu, R. Xie, H. Liu, H. Xiong, and Y. Fu, \u201cFair Graph Auto-Encoder for Unbiased Graph Representations with Wasserstein Distance,\u201d in 2021 IEEE International Conference on Data Mining (ICDM)<\/i>, 2021, pp. 1054\u20131059.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
A. M. Bourgault, R. Xie, S. Talbert, and M. L. Sole, \u201cAssociation of enteral feeding with microaspiration in critically ill adults,\u201d Applied Nursing Research<\/i>, vol. 67, p. 151611, 2022.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
R. Choudhury, J.-H. Park, L. Thiamwong, R. Xie, and J. R. Stout, \u201cObjectively Measured Physical Activity Levels and Associated Factors in Older US Women During the COVID-19 Pandemic: A Cross-sectional Study,\u201d JMIR Aging<\/i>, vol. 5, no. 3, p. e38172, 2022.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
X. Wan, N. R. Lighthall, and R. Xie, \u201cConsistent and robust predictors of Internet Use among older adults over time identified by machine learning,\u201d Computers in Human Behavior<\/i>, vol. 137, p. 107413, 2022.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
X. Xing, R. Xie, and W. Zhong, \u201cModel-based sparse coding beyond Gaussian independent model,\u201d Computational Statistics & Data Analysis<\/i>, vol. 166, p. 107336, 2022.<\/div>\n<\/div>\n<\/div>\n

2021<\/h3>\n
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L. Thiamwong, O. Garcia, R. Choudhury, J.-H. Park, J. Stout, and R. Xie, \u201cFeasibility and Acceptability of the Technology-Based Fall Risk Assessments for Older Adults,\u201d Innovation in Aging<\/i>, vol. 5, no. Suppl 1, p. 1004, 2021.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
A. Rossler et al.<\/i>, \u201cFear of Falling, Fall Risk, Depression, and Anxiety in Community-Dwelling Older Adults,\u201d Innovation in Aging<\/i>, vol. 5, no. Suppl 1, p. 1033, 2021.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
L. Thiamwong et al.<\/i>, \u201cAssociations among Fall Risk Appraisal, Body Composition, and Physical Activity in Older Adults,\u201d Innovation in Aging<\/i>, vol. 5, no. Suppl 1, p. 992, 2021.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
R. Choudhury, L. Thiamwong, O. Garcia, R. Xie, J. Stout, and J.-H. Park, \u201cAccelerometry-Based Assessment of Physical Activity in Older Adults During COVID-19 Pandemic,\u201d Innovation in Aging<\/i>, vol. 5, no. Supplement_1, pp. 1021\u20131022, 2021.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
C. Meng, R. Xie, A. Mandal, X. Zhang, W. Zhong, and P. Ma, \u201cLowcon: A design-based subsampling approach in a misspecified linear model,\u201d Journal of Computational and Graphical Statistics<\/i>, vol. 30, no. 3, pp. 694\u2013708, 2021.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
F. Li, F. Sun, N. Liu, and R. Xie, \u201cDenoising seismic signal via resampling local applicability functions,\u201d IEEE Geoscience and Remote Sensing Letters<\/i>, vol. 19, pp. 1\u20135, 2021.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
L. Ganzen et al.<\/i>, \u201cDrug screening with zebrafish visual behavior identifies carvedilol as a potential treatment for an autosomal dominant form of retinitis pigmentosa,\u201d Scientific Reports<\/i>, vol. 11, no. 1, pp. 1\u201314, 2021.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
R. Xie and D. Wu, \u201cOptimal transport-based transfer learning for smart manufacturing: Tool wear prediction using out-of-domain data,\u201d Manufacturing Letters<\/i>, vol. 29, pp. 104\u2013107, 2021.<\/div>\n<\/div>\n

2020<\/h3>\n<\/div>\n
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F. Li et al.<\/i>, \u201cOnline Distributed IoT Security Monitoring with Multidimensional Streaming Big Data,\u201d IEEE Internet of Things Journal<\/i>, 2020.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
Y. Lu, R. Xie, and S. Y. Liang, \u201cCEEMD-assisted kernel support vector machines for bearing diagnosis,\u201d The International Journal of Advanced Manufacturing Technology<\/i>, vol. 106, no. 7\u20138, pp. 3063\u20133070, 2020.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
Y. Lu, Z. Wang, R. Xie, J. Zhang, Z. Pan, and S. Y. Liang, \u201cBayesian optimized deep convolutional network for bearing diagnosis,\u201d The International Journal of Advanced Manufacturing Technology<\/i>, 2020.<\/div>\n<\/div>\n

2019 and prior<\/h3>\n<\/div>\n
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M. V. Nural, M. E. Cotterell, H. Peng, R. Xie, P. Ma, and J. A. Miller, \u201cAUTOMATED PREDICTIVE BIG DATA ANALYTICS USING ONTOLOGY BASED SEMANTICS,\u201d 2015.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
F. Li, R. Xie, W. Song, T. Zhao, K. Marfurt, and others, \u201cOptimal Lq norm regularization for sparse reflectivity inversion,\u201d 2017.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
X. Zhang, R. Xie, and P. Ma, \u201cStatistical Leveraging Methods in Big Data,\u201d in Handbook of Big Data Analytics<\/i>, Springer, 2018, pp. 51\u201374.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
Y. Lu, R. Xie, and S. Y. Liang, \u201cDetection of weak fault using sparse empirical wavelet transform for cyclic fault,\u201d The International Journal of Advanced Manufacturing Technology<\/i>, vol. 99, no. 5\u20138, pp. 1195\u20131201, 2018.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
Y. Lu, R. Xie, and S. Y. Liang, \u201cAdaptive online dictionary learning for bearing fault diagnosis,\u201d The International Journal of Advanced Manufacturing Technology<\/i>, 2018.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
R. Xie, L. Fangyu, W. Zengyan, and S. WenZhan, \u201cLarge scale randomized learning guided by physical laws with applications in full waveform inversion,\u201d 2018.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
R. Xie et al.<\/i>, \u201cNormalization of large-scale behavioural data collected from zebrafish,\u201d PLOS ONE<\/i>, vol. 14, no. 2, p. e0212234, 2019.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
Y. Lu, R. Xie, and S. Y. Liang, \u201cBearing fault diagnosis with nonlinear adaptive dictionary learning,\u201d The International Journal of Advanced Manufacturing Technology<\/i>, vol. 102, no. 9\u201312, pp. 4227\u20134239, 2019.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
R. Xie, Z. Wang, S. Bai, P. Ma, and W. Zhong, \u201cOnline Decentralized Leverage Score Sampling for Streaming Multidimensional Time Series,\u201d in The 22nd International Conference on Artificial Intelligence and Statistics<\/i>, 2019, pp. 2301\u20132311.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
F. Li, R. Xie, W.-Z. Song, and H. Chen, \u201cOptimal Seismic Reflectivity Inversion: Data-Driven l_p-Loss-l_q-Regularization Sparse Regression,\u201d IEEE Geoscience and Remote Sensing Letters<\/i>, vol. 16, pp. 806\u2013810, 2019.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
Y. Lu, R. Xie, and S. Y. Liang, \u201cExtraction of weak fault using combined dual-tree wavelet and improved MCA for rolling bearings,\u201d The International Journal of Advanced Manufacturing Technology<\/i>, 2019.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
Y. Lu, R. Xie, and S. Y. Liang, \u201cCEEMD-assisted bearing degradation assessment using tight clustering,\u201d The International Journal of Advanced Manufacturing Technology<\/i>, vol. 104, no. 1\u20134, pp. 1259\u20131267, 2019.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
Y. Lu, Z. Wang, R. Xie, and S. Liang, \u201cBayesian Optimized Deep Convolutional Network for Electrochemical Drilling Process,\u201d Journal of Manufacturing and Materials Processing<\/i>, vol. 3, no. 3, p. 57, 2019.<\/div>\n<\/div>\n
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\u00b7<\/div>\n
F. Li et al.<\/i>, \u201cDetection and Identification of Cyber and Physical Attacks on Distribution Power Grids with PVs: An Online High-Dimensional Data-driven Approach,\u201d IEEE Journal of Emerging and Selected Topics in Power Electronics<\/i>, 2019.<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Preprint W. Fan, S. Zheng, P. Wang, R. Xie, J. Bian, and Y. Fu, \u201cAddressing Distribution Shift in Time Series Forecasting with Instance Normalization Flows,\u201d arXiv preprint arXiv:2401.16777, 2024. \u00b7 X. Wang, D. Wang, W. Ying, R. Xie, H. Chen, and Y. Fu, \u201cKnockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent,\u201d arXiv preprint arXiv:2403.04015, […]<\/p>\n","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-9","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sciences.ucf.edu\/statistics\/aistats\/wp-json\/wp\/v2\/pages\/9","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sciences.ucf.edu\/statistics\/aistats\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sciences.ucf.edu\/statistics\/aistats\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sciences.ucf.edu\/statistics\/aistats\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/sciences.ucf.edu\/statistics\/aistats\/wp-json\/wp\/v2\/comments?post=9"}],"version-history":[{"count":5,"href":"https:\/\/sciences.ucf.edu\/statistics\/aistats\/wp-json\/wp\/v2\/pages\/9\/revisions"}],"predecessor-version":[{"id":103,"href":"https:\/\/sciences.ucf.edu\/statistics\/aistats\/wp-json\/wp\/v2\/pages\/9\/revisions\/103"}],"wp:attachment":[{"href":"https:\/\/sciences.ucf.edu\/statistics\/aistats\/wp-json\/wp\/v2\/media?parent=9"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}