Statistics - 2018

Lin, H., Tang, X., Shen, P., Zhang, D., Wu, J., Zhang, J., … Gao, P. (2018). Using big data to improve cardiovascular care and outcomes in China: a protocol for the CHinese Electronic health Records Research in Yinzhou (CHERRY) Study. BMJ Open, 8(2). https://doi.org/10.1136/bmjopen-2017-019698

Mollinari, M., Bode, A., Pereira, G. S., Gemenet, D. C., Khan, A., Kitavi, M. N., … Zeng, Z. (2018). B.Construction of an Ultradense Genetic Map in Hexaploid Sweetpotato. International Plant & Animal Genome XXVI Conference. Poster presented at the International Plant & Animal Genome XXVI Conference.

Zhao, M. J., Driscoll, A. R., Sengupta, S., Fricker, R. D., Jr, Spitzner, D. J., & Woodall, W. H. (2018). Performance evaluation of social network anomaly detection using a moving window-based scan method. Quality and Reliability Engineering International, 34(8), 1699–1716. https://doi.org/10.1002/qre.2364

Zhao, M. J., Driscoll, A. R., Sengupta, S., Stevens, N. T., Fricker, R. D., & Woodall, W. H. (2018). The effect of temporal aggregation level in social network monitoring. PLOS ONE, 13(12), e0209075. https://doi.org/10.1371/journal.pone.0209075

Sengupta, S. (2018). Anomaly detection in static networks using egonets. ArXiv Preprint ArXiv:1807.08925.

Sengupta, S., & Chen, Y. (2018). A block model for node popularity in networks with community structure Series B Statistical methodology.

The effect of temporal aggregation level in social network monitoring. (2018). Plos One, 13(12), e0209075.

Performance evaluation of social network anomaly detection using a moving window--based scan method. (2018). Quality and Reliability Engineering International, 34(8), 1699–1716.

Sengupta, S., & Woodall, W. H. (2018). Discussion of “Statistical methods for network surveillance.” Applied Stochastic Models in Business and Industry, 34(4), 446–448.

Sengupta, S., & Chen, Y. (2018). A block model for node popularity in networks with community structure. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(2), 365–386.

Schliep, E. M., Gelfand, A. E., Clark, J. S., & Kays, R. (2018). Joint Temporal Point Pattern Models for Proximate Species Occurrence in a Fixed Area Using Camera Trap Data. Journal of Agricultural, Biological and Environmental Statistics, 23(3), 334–357. https://doi.org/10.1007/s13253-018-0327-8

Schliep, E. M. (2018). Comments on: Process modeling for slope and aspect with application to elevation data maps. TEST, 27(4), 778–782. https://doi.org/10.1007/s11749-018-0620-4

Wagner, T., & Schliep, E. M. (2018). Combining nutrient, productivity, and landscape‐based regressions improves predictions of lake nutrients and provides insight into nutrient coupling at macroscales. Limnology and Oceanography, 63(6), 2372–2383. https://doi.org/10.1002/lno.10944

Gelfand, A. E., & Schliep, E. M. (2018). Bayesian Inference and Computing for Spatial Point Patterns. In NSF-CBMS Regional Conference Series in Probability and Statistics. https://doi.org/10.1214/cbms/1530065028

Schliep, E. M., Lany, N. K., Zarnetske, P. L., Schaeffer, R. N., Orians, C. M., Orwig, D. A., … Guisan, A. (2018). Joint species distribution modelling for spatio‐temporal occurrence and ordinal abundance data. Global Ecology and Biogeography, 27(1), 142–155. https://doi.org/10.1111/geb.12666

Schliep, E. M., Gelfand, A. E., Mitchell, R. M., Aiello‐Lammens, M. E., & Silander, J. A., Jr. (2018). Assessing the joint behaviour of species traits as filtered by environment. Methods in Ecology and Evolution, 9(3), 716–727. https://doi.org/10.1111/2041-210x.12901

Clark, J., Kueppers, L., Stover, D., Wyckoff, P., & Schliep, E. (2018). Disturbance and Vegetation Dynamics in Earth System Models: Workshop Report. https://doi.org/10.2172/1616531

Lany, N. K., Zarnetske, P. L., Schliep, E. M., Schaeffer, R. N., Orians, C. M., Orwig, D. A., & Preisser, E. L. (2018). Asymmetric biotic interactions and abiotic niche differences revealed by a dynamic joint species distribution model. Ecology, 99(5), 1018–1023. https://doi.org/10.1002/ecy.2190

Ghosh, S. (2018). Statistical Learning Methods Workshop. Presented at the Thammasat University, Bangkok, Thailand.

Li, Z., Wu, L., Zhou, W., & Ghosh, S. K. (2018). Multivariate Density Estimation with Missing Data (ArXiv No. 1808.04780).

Bhattacharya, I., Ghosal, R., & Ghosh, S. (2018). A Statistical Exploration of Duckworth-Lewis Method Using Bayesian Inference (ArXiv No. 1810.00908).

Ghosh, S. (2018, March). A Statistician's Journey: Astronomy to Zoology. Presented at the Mu Sigma Rho Banquet Speech, North Carolina State University, Raleigh, NC.

Ghosh, S. (2018, August). Bayesian Sample Size Determination Methods for Hypotheses Testing. Presented at the KOL Lecture Series, The DIA Bayesian Scientific Working Group.

Ghosh, S. (2018). Inference about Conditional Hazard Function without PH and AFT Assumptions. Presented at the ASA Biopharmaceutical Section Statistics Workshop, Washington, DC.

Ghosh, S. (2018). Does Knowledge of Shapes Matter in Statistics? Proceedings of the International Conference on Advances in Interdisciplinary Statistics and Combinatorics. Presented at the International Conference on Advances in Interdisciplinary Statistics and Combinatorics, University of North Carolina-Greensboro, Greensboro, NC.

Ghosh, S. (2018). When the Kendall's Tau or Spearman's Rho Are Not Good Measures of Dependence? Proceedings of the Southern Economic Association Conference. Presented at the Southern Economic Association Conference, Washington, DC.

Wu, S.-J., Ghosh, S. K., Ku, Y.-C., & Bloomfield, P. (2018). Dynamic correlation multivariate stochastic volatility with latent factors. Statistica Neerlandica, 72(1), 48–69. https://doi.org/10.1111/stan.12115

Janko, M. M., Irish, S. R., Reich, B. J., Peterson, M., Doctor, S. M., Mwandagalirwa, M. K., … Emch, M. E. (2018). The links between agriculture, Anopheles mosquitoes, and malaria risk in children younger than 5 years in the Democratic Republic of the Congo: a population-based, cross-sectional, spatial study. The Lancet Planetary Health, 2(2), e74–e82. https://doi.org/10.1016/S2542-5196(18)30009-3

Shi, C., Song, R., & Lu, W. (2018). Discussion of ’Optimal treatment allocations in space and time for on-line control of an emerging infectious disease’ [Review of Optimal treatment allocations in space and time for on-line control of an emerging infectious disease, by E. Laber, N. Meyer, B. Reich, K. Pacifici, J. Collazo, & J. Drake]. Journal of the Royal Statistical Society, Series C, 67(4), 775–776.

Yang, S., Kim, J. K., & Hwang, Y. (2018). Integration of survey and big observational data for finite population inference using mass imputation. Retrieved from https://arxiv.org/abs/1807.02817

Yang, S. (2018). Semiparametric efficient estimation of structural nested mean models with irregularly spaced observations. Retrieved from https://arxiv.org/abs/1810.00042

Guinness, J., & Hammerling, D. (2018). Compression and Conditional Emulation of Climate Model Output. Journal of the American Statistical Association, 113(521), 56–67. https://doi.org/10.1080/01621459.2017.1395339

Reich, B. J., & Haran, M. (2018). Precision maps for public health. Nature, 555(7694), 32–33. https://doi.org/10.1038/D41586-018-02096-W

Fisher, M. L., Watson, D. W., Osborne, J. A., Mochizuki, H., Breen, M., & Schal, C. (2018). Growth kinetics of endosymbiont Wolbachia in the common bed bug, Cimex lectularius. Scientific Reports, 8(1). https://doi.org/10.1038/S41598-018-29682-2

Cacho, J. F., Youssef, M. A., Shi, W., Chescheir, G. M., Skaggs, R. W., Tian, S., … Arellano, C. (2018). Impacts of forest-based bioenergy feedstock production on soil nitrogen cycling. Forest Ecology and Management, 419-420, 227–239. https://doi.org/10.1016/J.FORECO.2018.04.002

Cacho, J. F., Youssef, M. A., Chescheir, G. M., Wayne Skaggs, R., Appelboom, T. W., Leggett, Z. H., … Arellano, C. (2018). Effects of forest-based bioenergy feedstock production on shallow groundwater quality of a drained forest soil. Science of The Total Environment, 631-632, 13–22. https://doi.org/10.1016/J.SCITOTENV.2018.03.020

Chen, H., Wang, B., Geng, S., Arellano, C., Chen, S., & Qu, R. (2018). Effects of overexpression of jasmonic acid biosynthesis genes on nicotine accumulation in tobacco. Plant Direct, 2(1), e00036. https://doi.org/10.1002/PLD3.36

Newman, A. P., Rodríguez, Á. M., & Hunt, W. F. (2018). History and Heritage. World Environmental and Water Resources Congress 2018. Presented at the World Environmental and Water Resources Congress 2018. https://doi.org/10.1061/9780784481394.010

Smolek, A. P., Anderson, A. R., & Hunt, W. F., III. (2018). Hydrologic and Water-Quality Evaluation of a Rapid-Flow Biofiltration Device. Journal of Environmental Engineering, 144(2), 05017010. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001275

Jeng, X. J., Zhang, T., & Tzeng, J.-Y. (2018). Efficient Signal Inclusion With Genomic Applications. Journal of the American Statistical Association, 1–23. https://doi.org/10.1080/01621459.2018.1518236

Green, A. J., Hoyo, C., Mattingly, C. J., Luo, Y., Tzeng, J.-Y., Murphy, S. K., … Planchart, A. (2018). Cadmium exposure increases the risk of juvenile obesity: a human and zebrafish comparative study. International Journal of Obesity, 42(7), 1285–1295. https://doi.org/10.1038/S41366-018-0036-Y

Ghosh, S. K., Burns, C. B., Prager, D. L., Zhang, L., & Hui, G. (2018). On nonparametric estimation of the latent distribution for ordinal data. Computational Statistics & Data Analysis, 119, 86–98. https://doi.org/10.1016/J.CSDA.2017.10.001

Paul, E., Maity, A. K., & Maiti, R. (2018). Bayesian comparative study on binary time series. Journal of Statistical Computation and Simulation, 88(14), 2811–2826. https://doi.org/10.1080/00949655.2018.1488256

Maity, A. K., Pradhan, V., & Das, U. (2018). Bias Reduction in Logistic Regression with Missing Responses When the Missing Data Mechanism is Nonignorable. The American Statistician, 73(4), 340–349. https://doi.org/10.1080/00031305.2017.1407359

Shao, H., Wei, S., Jiang, X., Holmes, D. P., & Ghosh, T. K. (2018). Bistable Polymer Actuators: Bioinspired Electrically Activated Soft Bistable Actuators (Adv. Funct. Mater. 35/2018). Advanced Functional Materials, 28(35), 1870244. https://doi.org/10.1002/ADFM.201870244

Das, P., & Ghosal, S. (2018). Bayesian non-parametric simultaneous quantile regression for complete and grid data. Computational Statistics & Data Analysis, 127, 172–186. https://doi.org/10.1016/J.CSDA.2018.04.007

Laber, E. B., Rose, E. J., Davidian, M., & Tsiatis, A. A. (2018). Q-Learning. https://doi.org/10.1002/9781118445112.stat07998

Luo, S., Song, R., Styner, M., Gilmore, J. H., & Zhu, H. (2018). FSEM: Functional Structural Equation Models for Twin Functional Data. Journal of the American Statistical Association, 114(525), 344–357. https://doi.org/10.1080/01621459.2017.1407773

Zhu, W., Zeng, D., & Song, R. (2018). Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes. Journal of the American Statistical Association, 114(527), 1404–1417. https://doi.org/10.1080/01621459.2018.1506341

Xiao, L. (2018). Asymptotics of bivariate penalised splines. Journal of Nonparametric Statistics, 31(2), 289–314. https://doi.org/10.1080/10485252.2018.1563295

Chen, S. T., Xiao, L., & Staicu, A.-M. (2018). A Smoothing-based Goodness-of-Fit Test of Covariance for Functional Data. Biometrics, 11. https://doi.org/10.1111/biom.13005

Anderson, C., Xiao, L., & Checkley, W. (2018). Using data from multiple studies to develop a child growth correlation matrix. Statistics in Medicine, 4. https://doi.org/10.1002/sim.7696

Yang, S., & Kim, J. K. (2018). Discussion: Dissecting Multiple Imputation from a Multi-phase Inference Perspective: What Happens When God's, Imputer's and Analyst's Models are Uncongenial? by X. Xie and X. L. Meng. Statistica Sinica. https://doi.org/10.5705/ss.202016.0155

Geden, M., Staicu, A.-M., & Feng, J. (2018). The impacts of perceptual load and driving duration on mind wandering in driving. Transportation Research Part F: Traffic Psychology and Behaviour, 57, 75–83. https://doi.org/10.1016/J.TRF.2017.07.004

Tekbudak, M. Y., Alfaro-Córdoba, M., Maity, A., & Staicu, A.-M. (2018). A comparison of testing methods in scalar-on-function regression. AStA Advances in Statistical Analysis, 103(3), 411–436. https://doi.org/10.1007/S10182-018-00337-X

Laber, E. B., Wu, F., Munera, C., Lipkovich, I., Colucci, S., & Ripa, S. (2018). Identifying optimal dosage regimes under safety constraints: An application to long term opioid treatment of chronic pain. Statistics in Medicine, 37(9), 1407–1418. https://doi.org/10.1002/SIM.7566

Liang, S., Lu, W., & Song, R. (2018). Deep advantage learning for optimal dynamic treatment regime. Statistical Theory and Related Fields, 2(1), 80–88. https://doi.org/10.1080/24754269.2018.1466096

Zhou, Y.-H., Marron, J. S., & Wright, F. A. (2018). Eigenvalue Significance Testing for Genetic Association. Biometrics. https://doi.org/10.1111/biom.12767

Frayling, T. M., Beaumont, R. N., Jones, S. E., Yaghootkar, H., Tuke, M. A., Ruth, K. S., … others. (2018). A common allele in FGF21 associated with sugar intake is associated with body shape, lower total body-fat percentage, and higher blood pressure. Cell Reports, 23(2), 327–336.

Polineni, D., Dang, H., Gallins, P. J., Jones, L. C., Pace, R. G., Stonebraker, J. R., … others. (2018). Airway mucosal host defense is key to genomic regulation of cystic fibrosis lung disease severity. American Journal of Respiratory and Critical Care Medicine, 197(1), 79–93.

Kim, J. S., Maity, A., & Staicu, A.-M. (2018). Additive nonlinear functional concurrent model. Statistics and Its Interface, 11(4), 669–685. https://doi.org/10.4310/sii.2018.v11.n4.a11

Bandyopadhyay, S., & Maity, A. (2018). Asymptotic theory for varying coefficient regression models with dependent data. Annals of the Institute of Statistical Mathematics, 70(4), 745–759. https://doi.org/10.1007/s10463-017-0607-z

Davenport, C. A., Maity, A., Sullivan, P. F., & Tzeng, J.-Y. (2018). A Powerful Test for SNP Effects on Multivariate Binary Outcomes Using Kernel Machine Regression. Statistics in Biosciences, 10(1), 117–138. https://doi.org/10.1007/s12561-017-9189-9

Lahiri, S. N. (2018). Uncertainty Quantification in Robust Inference for Irregularly Spaced Spatial Data Using Block Bootstrap. SANKHYA-SERIES A-MATHEMATICAL STATISTICS AND PROBABILITY, 80, 173–221. https://doi.org/10.1007/s13171-018-0154-6

Du, X., & Ghosal, S. (2018). Bayesian Discriminant Analysis Using a High Dimensional Predictor. Sankhya A, 80(S1), 112–145. https://doi.org/10.1007/s13171-018-0140-z

Wang, C., Shin, S. J., & Wu, Y. (2018). Principal quantile regression for sufficient dimension reduction with heteroscedasticity. ELECTRONIC JOURNAL OF STATISTICS, 12(2), 2114–2140. https://doi.org/10.1214/18-EJS1432

Chen, D., Fu, W., Krishna, R., & Menzies, T. (2018). Applications of Psychological Science for Actionable Analytics. ESEC/FSE'18: PROCEEDINGS OF THE 2018 26TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, pp. 456–467. https://doi.org/10.1145/3236024.3236050

Li, X., & Ghosal, S. (2018). Bayesian classification of multiclass functional data. ELECTRONIC JOURNAL OF STATISTICS, 12(2), 4669–4696. https://doi.org/10.1214/18-EJS1522

Larson, G., Thorne, J. L., & Schmidler, S. (2018). Modeling Dependence in Evolutionary Inference for Proteins. RESEARCH IN COMPUTATIONAL MOLECULAR BIOLOGY, RECOMB 2018, Vol. 10812, pp. 122–137. https://doi.org/10.1007/978-3-319-89929-9_8

Hager, R., Tsiatis, A. A., & Davidian, M. (2018). Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data. BIOMETRICS, 74(4), 1180–1192. https://doi.org/10.1111/biom.12894

Zhang, Y., Laber, E. B., Davidian, M., & Tsiatis, A. A. (2018). Interpretable Dynamic Treatment Regimes. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 113(524), 1541–1549. https://doi.org/10.1080/01621459.2017.1345743

Shi, C., Lu, W., & Song, R. (2018). A Massive Data Framework for M-Estimators with Cubic-Rate. Journal of the American Statistical Association, 113(524), 1698–1709. https://doi.org/10.1080/01621459.2017.1360779

Guinness, J. (2018). Permutation and Grouping Methods for Sharpening Gaussian Process Approximations. TECHNOMETRICS, 60(4), 415–429. https://doi.org/10.1080/00401706.2018.1437476

Geden, M., Staicu, A.-M., & Feng, J. (2018). Reduced Target Facilitation and Increased Distractor Suppression During Mind Wandering. EXPERIMENTAL PSYCHOLOGY, 65(6), 345–352. https://doi.org/10.1027/1618-3169/a000417

House, J. S., Mendez, M., Maguire, R. L., Gonzalez-Nahm, S., Huang, Z., Daniels, J., … Hoyo, C. (2018). Periconceptional Maternal Mediterranean Diet Is Associated With Favorable Offspring Behaviors and Altered CpG Methylation of Imprinted Genes. FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY, 6. https://doi.org/10.3389/fcell.2018.00107

Balko, J. A., Gatson, B. J., Cohen, E. B., Griffith, E. H., Harms, C. A., & Bailey, K. M. (2018). INHALANT ANESTHETIC RECOVERY FOLLOWING INTRAMUSCULAR EPINEPHRINE IN THE LOGGERHEAD SEA TURTLE (CARETTA CARETTA). JOURNAL OF ZOO AND WILDLIFE MEDICINE, 49(3), 680–688. https://doi.org/10.1638/2017-0182.1

Rieger, T. R., Allen, R. J., Bystricky, L., Chen, Y., Colopy, G. W., Cui, Y., … Musante, C. J. (2018). [Review of Improving the generation and selection of virtual populations in quantitative systems pharmacology models]. PROGRESS IN BIOPHYSICS & MOLECULAR BIOLOGY, 139, 15–22. https://doi.org/10.1016/j.pbiomolbio.2018.06.002

Dai, H., Shahzad, M., Liu, A. X., Li, M., Zhong, Y., & Chen, G. (2018). Identifying and Estimating Persistent Items in Data Streams. IEEE-ACM TRANSACTIONS ON NETWORKING, 26(6), 2429–2442. https://doi.org/10.1109/TNET.2018.2865125

Hughes-Oliver, J. M., Xu, G., & Baynes, R. E. (2018). Skin Permeation of Solutes from Metalworking Fluids to Build Prediction Models and Test A Partition Theory. MOLECULES, 23(12). https://doi.org/10.3390/molecules23123076

Ceyhan, E. (2018). A contingency table approach based on nearest neighbour relations for testing self and mixed correspondence. SORT-STATISTICS AND OPERATIONS RESEARCH TRANSACTIONS, 42(2), 125–157. https://doi.org/10.2436/20.8080.02.72

Chen, S. T., Luo, Xi, Hou, R., Raubenheimer, D., Ji, W., Jin, X., … Li, B. (2018). Nutrient Balancing by Captive Golden Snub-Nosed Monkeys (Rhinopithecus roxellana). INTERNATIONAL JOURNAL OF PRIMATOLOGY, 39(6), 1124–1138. https://doi.org/10.1007/s10764-018-0070-6

Ning, B., Wolfgang, A., & Ghosh, S. (2018). Predicting Exoplanet Masses and Radii: A Nonparametric Approach. ASTROPHYSICAL JOURNAL, 869(1). https://doi.org/10.3847/1538-4357/aaeb31

Spivey, T. A., Wilkerson, G. G., Buol, G. S., Edmisten, K. L., & Barnes, E. M. (2018). USE OF CSM-CROPGRO-COTTON TO DETERMINE THE AGRONOMIC AND ECONOMIC VALUE OF IRRIGATION TO UPLAND COTTON PRODUCTION IN NORTH AND SOUTH CAROLINA. TRANSACTIONS OF THE ASABE, 61(5), 1627–1638. https://doi.org/10.13031/trans.12801

Gaines, B. R., Kim, J., & Zhou, H. (2018). Algorithms for Fitting the Constrained Lasso. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 27(4), 861–871. https://doi.org/10.1080/10618600.2018.1473777

Sparks, C. R., Gorney, A., Williams, K., Griffith, E. H., Cerda-Gonzalez, S., Lascelles, B. D. X., & Olby, N. J. (2018). Investigation of sensory thresholds in Cavalier King Charles Spaniels with and without Chiari-like malformations and syringomyelia. JOURNAL OF VETERINARY INTERNAL MEDICINE, 32(6), 2021–2028. https://doi.org/10.1111/jvim.15297

Stevenson, K. T., Peterson, M. N., & Bondell, H. D. (2018). Developing a model of climate change behavior among adolescents. CLIMATIC CHANGE, 151(3-4), 589–603. https://doi.org/10.1007/s10584-018-2313-0

Banks, H. T., Kabanikhin, S. I., Krivorotko, O. I., & Yermolenko, D. V. (2018). A numerical algorithm for constructing an individual mathematical model of HIV dynamics at cellular level. JOURNAL OF INVERSE AND ILL-POSED PROBLEMS, 26(6), 859–873. https://doi.org/10.1515/jiip-2018-0019

Rutter, E. M., Banks, H. T., & Flores, K. B. (2018). Estimating intratumoral heterogeneity from spatiotemporal data. JOURNAL OF MATHEMATICAL BIOLOGY, 77(6-7), 1999–2022. https://doi.org/10.1007/s00285-018-1238-6

Grimm, F. A., Blanchette, A., House, J. S., Ferguson, K., Hsieh, N.-H., Dalaijamts, C., … Rusyn, I. (2018). A Human Population-Based Organotypic In Vitro Model for Cardiotoxicity Screening. ALTEX-ALTERNATIVES TO ANIMAL EXPERIMENTATION, 35(4), 441–452. https://doi.org/10.14573/altex.1805301

Xu, J., Chi, E. C., Yang, M., & Lange, K. (2018). A majorization-minimization algorithm for split feasibility problems. COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 71(3), 795–828. https://doi.org/10.1007/s10589-018-0025-z

Selm, K. R., Hess, G. R., Peterson, M. N., Beck, S. M., & McHale, M. R. (2018). Developing an Instrument to Measure Autonomous Adaptive Capacity to Climate Change among Urban Households. FRONTIERS IN ECOLOGY AND EVOLUTION, 6. https://doi.org/10.3389/fevo.2018.00013

Ash, J. R., & Hughes-Oliver, J. M. (2018). chemmodlab: a cheminformatics modeling laboratoryR package for fitting and assessing machine learning models. JOURNAL OF CHEMINFORMATICS, 10. https://doi.org/10.1186/s13321-018-0309-4

Matli, V. R. R., Fang, S., Guinness, J., Rabalais, N. N., Craig, J. K., & Obenour, D. R. (2018). Space-Time Geostatistical Assessment of Hypoxia in the Northern Gulf of Mexico. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 52(21), 12484–12493. https://doi.org/10.1021/acs.est.8b03474

Chatterjee, A., & Lahiri, S. N. (2018). EDGEWORTH EXPANSIONS FOR A CLASS OF SPECTRAL DENSITY ESTIMATORS AND THEIR APPLICATIONS TO INTERVAL ESTIMATION. STATISTICA SINICA, 28(4), 2591–2608. https://doi.org/10.5705/ss.202017.0121

Tak, H., Ghosh, S. K., & Ellis, J. A. (2018). How proper are Bayesian models in the astronomical literature? MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 481(1), 277–285. https://doi.org/10.1093/mnras/sty2326

Kang, S., Lu, W., & Zhang, J. (2018). ON ESTIMATION OF THE OPTIMAL TREATMENT REGIME WITH THE ADDITIVE HAZARDS MODEL. STATISTICA SINICA, 28(3), 1539–1560. https://doi.org/10.5705/ss.202016.0543

Yang, S., & Lok, J. J. (2018). SENSITIVITY ANALYSIS FOR UNMEASURED CONFOUNDING IN COARSE STRUCTURAL NESTED MEAN MODELS. STATISTICA SINICA, 28(4), 1703–1723. https://doi.org/10.5705/ss.202016.0133

Kong, D., Bondell, H. D., & Wu, Y. (2018). FULLY EFFICIENT ROBUST ESTIMATION, OUTLIER DETECTION AND VARIABLE SELECTION VIA PENALIZED REGRESSION. STATISTICA SINICA, 28(2), 1031–1052. https://doi.org/10.5705/ss.202016.0441

Banks, H. T., & Thompson, W. C. (2018). RANDOM DELAY DIFFERENTIAL EQUATIONS AND INVERSE PROBLEMS FOR AGGREGATE DATA PROBLEMS. EURASIAN JOURNAL OF MATHEMATICAL AND COMPUTER APPLICATIONS, 6(4), 4–16. https://doi.org/10.32523/2306-6172-2018-6-4-4-16

Adler, L. S., Michaud, K. M., Ellner, S. P., McArt, S. H., Stevenson, P. C., & Irwin, R. E. (2018). Disease where you dine: plant species and floral traits associated with pathogen transmission in bumble bees. ECOLOGY, 99(11), 2535–2545. https://doi.org/10.1002/ecy.2503

Bokil, V. A., Banks, H. T., Cioranescu, D., & Griso, G. (2018). A MULTISCALE METHOD FOR COMPUTING EFFECTIVE PARAMETERS OF COMPOSITE ELECTROMAGNETIC MATERIALS WITH MEMORY EFFECTS. QUARTERLY OF APPLIED MATHEMATICS, 76(4), 713–738. https://doi.org/10.1090/qam/1503

Irizarry, A. D., Collazo, J. A., Pacifici, K., Reich, B. J., & Battle, K. E. (2018). Avian response to shade-layer restoration in coffee plantations in Puerto Rico. RESTORATION ECOLOGY, 26(6), 1212–1220. https://doi.org/10.1111/rec.12697

Deaton, J. C., Chakraborty, A., Czerwieniec, R., Yersin, H., & Castellano, F. N. (2018). Temperature dependence of photophysical properties of a dinuclear CCN-cyclometalated Pt(II) complex with an intimate Pt-Pt contact. Zero-field splitting and sub-state decay rates of the lowest triplet. PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 20(38), 25096–25104. https://doi.org/10.1039/c8cp05213a

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