Publications

Chiou, S., Xu, G., Yan, J., & Huang, C.-Y. (2023). Regression modeling for recurrent events possibly with an informative terminal event using R package reReg. Journal of Statistical Software, 105(5), 1–34. https://doi.org/10.18637/jss.v105.i05

Lautier, J. P., Pozdnyakov, V., & Yan, J. (2023). Estimating a discrete distribution subject to random left-truncation with an application to structured finance. Econometrics and Statistics. https://doi.org/10.1016/j.ecosta.2023.05.005

Lautier, J. P., Pozdnyakov, V., & Yan, J. (2023). Pricing time-to-event contingent cash flows: A discrete-time survival analysis approach. Insurance: Mathematics and Economics, 110, 53–71. https://doi.org/10.1016/j.insmatheco.2023.02.003

Li, Y., Chen, K., Yan, J., & Zhang, X. (2023). Regularized fingerprinting in detection and attribution of climate change with weight matrix optimizing the efficiency in scaling factor estimation. Annals of Applied Statistics, 17(1), 225–239. https://doi.org/10.1214/22-AOAS1624

Wang, F., Wang, H., & Yan, J. (2023). Diagnostic tests for the necessity of weight in regression with survey data. International Statistical Review, 91(1), 55–71. https://doi.org/10.1111/insr.12509

Wang, W., Luo, C., Aseltine, R. H., Wang, F., Yan, J., & Chen, K. (2023). Survival modeling of suicide risk with rare and uncertain diagnoses. Statistics in Biosciences. https://doi.org/10.1007/s12561-023-09374-w

Hu, C., Pozdnyakov, V., & Yan, J. (2022). On occupation time for on-off processes with multiple off-states. Modern Stochastics: Theory and Applications, 9(4), 413–430. https://doi.org/10.15559/22-VMSTA210

Jiao, J., Tang, Z., Yue, M., Zhang, P., & Yan, J. (2022). Cyberattack-resilient load forecasting with adaptive robust regression. International Journal of Forecasting, 38(3), 910–919. https://doi.org/10.1016/j.ijforecast.2021.06.009

Lau, A. Y. Z., & Yan, J. (2022). Bias analysis of generalized estimating equations under measurement error and practical bias correction. Stat, 11(1), e418. https://doi.org/10.1002/sta4.418

Price, M., & Yan, J. (2022). The effects of the NBA COVID bubble on the NBA playoffs: A case study for home-court advantage. American Journal of Undergraduate Research, 18(4), 3–15. https://doi.org/10.33697/ajur.2022.051

Sun, Q., Zwiers, F., Zhang, X., & Yan, J. (2022). Quantifying the human influence on the intensity of extreme 1-and 5-day precipitation amounts at global, continental, and regional scales. Journal of Climate, 35(1), 195–210. https://doi.org/10.1175/JCLI-D-21-0028.1

Wang, T., Yan, J., Yuan, Y., & Zhang, P. (2022). Generating directed networks with predetermined assortativity measures. Statistics and Computing, 32(5), 1–15. https://doi.org/10.1007/s11222-022-10161-8

Xiao, S., Yan, J., & Zhang, P. (2022). Incorporating auxiliary information in betweenness measure for input–output networks. Physica A: Statistical Mechanics and Its Applications, 607, 128200. https://doi.org/10.1016/j.physa.2022.128200

Yang, Z., Wang, H., & Yan, J. (2022). Optimal subsampling for parametric accelerated failure time models with massive survival data. Statistics in Medicine, 41(27), 5421–5431. https://doi.org/10.1002/sim.9576

Yin, F., Jiao, J., Yan, J., & Hu, G. (2022). Bayesian nonparametric learning for point processes with spatial homogeneity: A spatial analysis of NBA shot locations. Proceedings of the 39th International Conference on Machine Learning, 162, 25523–25551.

Zhang, P., Wang, T., & Yan, J. (2022). PageRank centrality and algorithms for weighted, directed networks. Physica A: Statistical Mechanics and Its Applications, 586, 126438. https://doi.org/10.1016/j.physa.2021.126438

Chang, C. F., Garcia, V., Tang, C., Vlahos, P., Wanik, D., Yan, J., Bash, J. O., & Astitha, M. (2021). Linking multi-media modeling with machine learning to assess and predict lake chlorophyll \(\alpha\) concentrations. Journal of Great Lakes Research, 47(6), 1656–1670. https://doi.org/10.1016/j.jglr.2021.09.011

Chang, S.-Y., Jin, J., Yan, J., Dong, X., Chaudhuri, B., Nagapudi, K., & Ma, A. W. K. (2021). Development of a pilot-scale HuskyJet binder jet 3D printer for additive manufacturing of pharmaceutical tablets. International Journal of Pharmaceutics, 605, 120791. https://doi.org/10.1016/j.ijpharm.2021.120791

Hu, C., Elbroch, M., Meyer, T., Pozdnyakov, V., & Yan, J. (2021). Moving-resting process with measurement error in animal movement modeling. Methods in Ecology and Evolution, 12(11), 2221–2233. https://doi.org/10.1111/2041-210X.13694

Jiao, J., Hu, G., & Yan, J. (2021). A Bayesian marked spatial point processes model for basketball shot chart. Journal of Quantitative Analysis in Sports, 17(2), 77–90. https://doi.org/10.1515/jqas-2019-0106

Jiao, J., Hu, G., & Yan, J. (2021). Heterogeneity pursuit for spatial point pattern with application to tree locations: A Bayesian semiparametric recourse. Environmetrics, 32(7), e2694. https://doi.org/10.1002/env.2694

Li, Y., Chen, K., Yan, J., & Zhang, X. (2021). Uncertainty in optimal fingerprinting is underestimated. Environmental Research Letters, 16(8), 084043. https://doi.org/10.1088/1748-9326/ac14ee

Wang, T., Xiao, S., Yan, J., & Zhang, P. (2021). Regional and sectoral structures of the Chinese economy: A network perspective from multi-regional input-output tables. Physica A: Statistical Mechanics and Its Applications, 126196. https://doi.org/10.1016/j.physa.2021.126196

Wang, T., & Yan, J. (2021). Discussion of “on studying extreme values and systematic risks with nonlinear time series models and tail dependence measure". Statistical Theory and Related Fields, 5(1), 38–40. https://doi.org/10.1080/24754269.2020.1869897

Wang, W., & Yan, J. (2021). Shape-restricted regression splines with R package splines2. Journal of Data Science, 19(3), 498–517. https://doi.org/10.6339/21-JDS1020

Wang, Z., Jiang, Y., Wan, H., Yan, J., & Zhang, X. (2021). Toward optimal fingerprinting in detection and attribution of changes in climate extremes. Journal of the American Statistical Association, 116(533), 1–13. https://doi.org/10.1080/01621459.2020.1730852

Wu, J., Chen, M.-H., Schifano, E. D., & Yan, J. (2021). Online updating of survival analysis. Journal of Computational and Graphical Statistics, 30(4), 1209–1223. https://doi.org/10.1080/10618600.2020.1870481

Xue, Y., Yan, J., & Schifano, E. D. (2021). Simultaneous monitoring for regression coefficients and baseline hazard profile in Cox modeling of time-to-event data. Biostatistics, 22(4), 756–771. https://doi.org/10.1093/biostatistics/kxz064

Yuan, Y., Yan, J., & Zhang, P. (2021). Assortativity measures for weighted and directed networks. Journal of Complex Networks, 9(2), cnab017. https://doi.org/10.1093/comnet/cnab017

Doshi, R. P., Yan, J., & Aseltine Jr, R. H. (2020). Age differences in racial/ethnic disparities in preventable hospitalizations for heart failure in Connecticut, 2009-2015: A population-based longitudinal study. Public Health Reports, 135(1), 56–65. https://doi.org/10.1177/0033354919884306

Hu, C., Pozdnyakov, V., & Yan, J. (2020). Density and distribution evaluation for convolution of independent gamma variables. Computational Statistics, 35(1), 327–342. https://doi.org/10.1007/s00180-019-00924-9

Jiang, Y., Lee, M.-L. T., He, X., Rosner, B., & Yan, J. (2020). Wilcoxon rank-based tests for clustered data with R package clusrank. Journal of Statistical Software, 96(6), 1–26. https://doi.org/10.18637/jss.v096.i06

Li, Y., Li, Y., Qin, Y., & Yan, J. (2020). Copula modeling for data with ties. Statistics and Its Interface, 13(1), 103–117. https://doi.org/10.4310/SII.2020.v13.n1.a9

Pozdnyakov, V., Elbroch, L. M., Hu, C., Meyer, T., & Yan, J. (2020). On estimation for Brownian motion governed by telegraph process with multiple off states. Methodology and Computing in Applied Probability, 22, 1275–1291. https://doi.org/10.1007/s11009-020-09774-1

Vaughan, G., Aseltine, R., Chen, K., & Yan, J. (2020). Efficient interaction selection for clustered data via stagewise generalized estimating equations. Statistics in Medicine, 39(22), 2855–2868. https://doi.org/10.1002/sim.8574

Wang, C., Schifano, E. D., & Yan, J. (2020). Geographical ratings with spatial random effects in a two-part model. Variance, 13(1), 141–160.

Wang, W., Aseltine, R., Chen, K., & Yan, J. (2020). Integrative survival analysis with uncertain event times in application to a suicide risk study. Annals of Applied Statistics, 14(1), 51–73. https://doi.org/10.1214/19-AOAS1287

Xu, G., Chiou, S. H., Yan, J., Marr, K., & Huang, C.-Y. (2020). Generalized scale-change models for recurrent event processes under informative censoring. Statistica Sinica, 30(4), 1773–1795. https://doi.org/10.5705/ss.202018.0224

Xue, Y., Wang, H., Yan, J., & Schifano, E. D. (2020). An online updating approach for testing the proportional hazards assumption with streams of survival data. Biometrics, 76(1), 171–182. https://doi.org/10.1111/biom.13137

Aseltine, R. H., Wang, W., Benthien, R. A., Katz, M., Wagner, C., Yan, J., & Lewis, C. G. (2019). Reductions in race and ethnic disparities in hospital readmissions following total joint arthroplasty from 2005 to 2015. Journal of Bone and Joint Surgery, 101(22), 2044–2050. https://doi.org/10.2106/JBJS.18.01112

Caplan, D. J., Li, Y., Wang, W., Kang, S., Marchini, L., Cowen, H., & Yan, J. (2019). Dental restoration longevity among geriatric and special needs patients. JDR Clinical & Translational Research, 4(1), 41–48. https://doi.org/10.1177/2380084418799083

Chiou, S. H., Huang, C.-Y., Xu, G., & Yan, J. (2019). Semiparametric regression analysis of panel count data: A practical review. International Statistical Review, 87(1), 24–43. https://doi.org/10.1111/insr.12271

Pozdnyakov, V., Elbroch, L. M., Labarga, A., Meyer, T., & Yan, J. (2019). Discretely observed Brownian motion governed by telegraph process: Estimation. Methodology and Computing in Applied Probability, 21(3), 907–920. https://doi.org/10.1007/s11009-017-9547-6

Bader, B., Yan, J., & Zhang, X. (2018). Automated threshold selection for extreme value analysis via goodness-of-fit tests with application to batched return level mapping. Annals of Applied Statistics, 12(1), 310–329. https://doi.org/10.1214/17-AOAS1092

Chiou, S. H., Xu, G., Yan, J., & Huang, C.-Y. (2018). Semiparametric estimation of the accelerated mean model with panel count data under informative examination times. Biometrics, 74(3), 944–953. https://doi.org/10.1111/biom.12840

Hofert, M., Kojadinovic, I., Mächler, M., & Yan, J. (2018). Elements of copula modeling with R. Springer. https://doi.org/10.1007/978-3-319-89635-9

Wang, C., Chen, M.-H., Wu, J., Yan, J., Zhang, Y., & Schifano, E. (2018). Online updating method with new variables for big data streams. Canadian Journal of Statistics, 46(1), 123–146. https://doi.org/10.1002/cjs.11330

Bader, B., Yan, J., & Zhang, X. (2017). Automated selection of \(r\) for the \(r\) largest order statistics approach with adjustment for sequential testing. Statistics and Computing, 27(6), 1435–1451. https://doi.org/10.1007/s11222-016-9697-3

Vaughan, G., Aseltine, R., Chen, K., & Yan, J. (2017). Stagewise generalized estimating equations with grouped variables. Biometrics, 73(4), 1332–1342. https://doi.org/10.1111/biom.12669

Wang, Z., Jiang, Y., Wan, H., Yan, J., & Zhang, X. (2017). Detection and attribution of changes in extreme temperatures at regional level. Journal of Climate, 30(17), 7035–7047. https://doi.org/10.1175/jcli-d-15-0835.1

Xu, G., Chiou, S., Huang, C.-Y., Wang, M.-C., & Yan, J. (2017). Joint scale-change models for recurrent events and failure time. Journal of the American Statistical Association, 112, 794–805. https://doi.org/10.1080/01621459.2016.1173557

Olayiwola, J. N., Anderson, D., Jepeal, N., Aseltine, R., Pickett, C., Yan, J., & Zlateva, I. (2016). Electronic consultations to improve the primary care-specialty care interface for cardiology in the medically underserved: A cluster-randomized controlled trial. Annals of Family Medicine, 14(2), 133–140. https://doi.org/10.1370/afm.1869

Schifano, E. D., Wu, J., Wang, C., Yan, J., & Chen, M.-H. (2016). Online updating of statistical inference in the big data setting. Technometrics, 58(3), 393–403. https://doi.org/10.1080/00401706.2016.1142900

Vaughan, G., Aseltine, R., Chiou, S. H., & Yan, J. (2016). An alarm system for flu outbreaks using google flu trend data. In J. Lin, B. Wang, X. Hu, K. Chen, & R. Liu (Eds.), Statistical applications from clinical trials and personalized medicine to finance and business analytics: Selected papers from the 2015 ICSA/Graybill applied statistics symposium, colorado state university, fort collins (pp. 293–304). Springer International Publishing. https://doi.org/10.1007/978-3-319-42568-9_22

Wang, C., Chen, M.-H., Schifano, E., Wu, J., & Yan, J. (2016). Statistical methods and computing for big data. Statistics and Its Interface, 9(4), 399–414. https://doi.org/10.4310/SII.2016.v9.n4.a1

Wang, W., Chen, M.-H., Chiou, S. H., Lai, H.-C., Wang, X., Yan, J., & Zhang, Z. (2016). Onset of persistent pseudomonas aeruginosa infection in children with cystic fibrosis with interval censored data. BMC Medical Research Methodology, 16(122), 1–10. https://doi.org/10.1186/s12874-016-0220-5

Aseltine, Jr., R, Yan, J., Fleischman, S., Katz, M., & DeFrancesco, M. (2015). Racial and ethnic disparities in hospital readmissions after delivery. Obstetrics and Gynecology, 126(5), 1040–1047. https://doi.org/10.1097/aog.0000000000001090

Aseltine, R. H., Yan, J., Gruss, C. B., Wagner, C., & Katz, M. (2015). Connecticut hospital readmissions related to chest pain and heart failure: Differences by race, ethnic, and payer. Connecticut Medicine, 79(2), 69–76.

Chi, Z., Pozdnyakov, V., & Yan, J. (2015). On expected occupation time of Brownian bridge. Statistics and Probability Letters, 97, 83–87. https://doi.org/10.1016/j.spl.2014.11.009

Chiou, S. H., Kang, S., & Yan, J. (2015). Rank-based estimating equations with general weight for accelerated failure time models: An induced smoothing approach. Statistics in Medicine, 34(9), 1495–1510. https://doi.org/10.1002/sim.6415

Chiou, S. H., Kang, S., & Yan, J. (2015). Semiparametric accelerated failure time modeling for clustered failure times from stratified sampling. Journal of the American Statistical Association, 110(510), 621–629. https://doi.org/10.1080/01621459.2014.917978

Chiou, S., Kang, S., & Yan, J. (2015). Change point analysis of top baseball batting average. In D. K. Dey & J. Yan (Eds.), Extreme value modeling and risk analysis: Methods and applications (pp. 493–504). Taylor & Francis. https://doi.org/10.1201/b19721-28

Dey, D. K., Roy, D., & Yan, J. (2015). Univariate extreme value analysis. In D. K. Dey & J. Yan (Eds.), Extreme value modeling and risk analysis: Methods and applications (pp. 1–22). Taylor & Francis. https://doi.org/10.1201/b19721-5

Dey, D. K., & Yan, J. (Eds.). (2015). Extreme value modeling and risk analysis: Methods and applications. Taylor & Francis. https://doi.org/10.1201/b19721

Jiang, Y., Dey, D. K., & Yan, J. (2015). Multivariate extreme value analysis. In D. K. Dey & J. Yan (Eds.), Extreme value modeling and risk analysis: Methods and applications (pp. 23–39). Taylor & Francis. https://doi.org/10.1201/b19721-6

Kojadinovic, I., Shang, H., & Yan, J. (2015). A class of goodness-of-fit tests for spatial extremes models based on max-stable processes. Statistics and Its Interface, 8(1), 45–62. https://doi.org/10.4310/sii.2015.v8.n1.a5

Prates, M. O., Dey, D. K., Willig, M. R., & Yan, J. (2015). Transformed Gaussian Markov random fields and spatial modeling of species abundance. Spatial Statistics, 14, 382–399. https://doi.org/10.1016/j.spasta.2015.07.004

Shang, H., Yan, J., & Zhang, X. (2015). A two-step approach to model precipitation extremes in California based on max-stable and marginal point processes. The Annals of Applied Statistics, 9(1), 452–473. https://doi.org/10.1214/14-aoas804

Chiou, S. H., Kang, S., & Yan, J. (2014). Fitting accelerated failure time models in routine survival analysis with R package aftgee. Journal of Statistical Software, 61(11), 1–23. https://doi.org/10.18637/jss.v061.i11

Chiou, S., Kang, S., Kim, J., & Yan, J. (2014). Marginal semiparametric multivariate accelerated failure time model with generalized estimating equations. Lifetime Data Analysis, 20(4), 599–618. https://doi.org/10.1007/s10985-014-9292-x

Chiou, S., Kang, S., & Yan, J. (2014). Fast accelerated failure time modeling for case-cohort data. Statistics and Computing, 24(4), 559–568. https://doi.org/10.1007/s11222-013-9388-2

Pozdnyakov, V., Meyer, T., Wang, Y.-B., & Yan, J. (2014). On modeling animal movements using Brownian motion with measurement error. Ecology, 95(2), 247–253. https://doi.org/10.1890/13-0532.1

Wang, Z., Yan, J., & Zhang, X. (2014). Incorporating spatial dependence in regional frequency analysis. Water Resources Research, 50(12), 9570–9585. https://doi.org/10.1002/2013WR014849

Yan, J., Chen, Y., Lawrence-Apfel, K., Ortega, I. M., Pozdnyakov, V., Williams, S., & Meyer, T. (2014). A moving–resting process with an embedded Brownian motion for animal movements. Population Ecology, 56(2), 401–415. https://doi.org/10.1007/s10144-013-0428-8

Yan, J., Guo, C., & Paarlberg, L. E. (2014). Are antipoverty nonprofit organizations located where they are needed? Spatial analysis of the Greater Hartford region. American Statistician, 68(4), 243–252. https://doi.org/10.1080/00031305.2014.955211

Prates, M. O., Aseltine, R. H., Dey, D. K., & Yan, J. (2013). Assessing intervention efficacy on high-risk drinkers using generalized linear mixed models with a new class of link functions. Biometrical Journal, 55(6), 912–924. https://doi.org/10.1002/bimj.201300015

Wang, X., Chen, M.-H., & Yan, J. (2013). Bayesian dynamic regression models for interval censored survival data with application to children dental health. Lifetime Data Analysis, 19(3), 297–316. https://doi.org/10.1007/s10985-013-9246-8

Wang, X., Ma, S., & Yan, J. (2013). Augmented estimating equations for semiparametric panel count regression with informative observation times and censoring time. Statistica Sinica, 23(1), 359–381. https://doi.org/10.5705/ss.2010.297

Wang, X., & Yan, J. (2013). Practical notes on multivariate modeling based on elliptical copulas. Journal de La Société Française de Statistique, 154(1), 102–115.

Yan, J., Aseltine, R. H., Jr., & Harel, O. (2013). Comparing regression coefficients between nested linear models for clustered data with generalized estimating equations. Journal of Educational and Behavioral Statistics, 38(2), 172–189. https://doi.org/10.3102/1076998611432175

Cavallo, A., Rosenthal, B., Wang, X., & Yan, J. (2012). Treatment of the data collection threshold in operational risk: A case study using the lognormal distribution. Journal of Operational Risk, 7(1), 3–38. https://doi.org/10.21314/jop.2012.101

Chen, D. C. R., Kirshenbaum, D. S., Yan, J., Kirshenbaum, E., & Aseltine, R. H. (2012). Characterizing changes in student empathy throughout medical school. Medical Teacher, 34(4), 305–311. https://doi.org/10.3109/0142159x.2012.644600

Havens, E. K., Martin, K. S., Yan, J., Dauser-Forrest, D., & Ferris, A. M. (2012). Federal nutrition program changes and healthy food availability. American Journal of Preventive Medicine, 43(4), 419–422. https://doi.org/10.1016/j.amepre.2012.06.009

Kojadinovic, I., & Yan, J. (2012). A non-parametric test of exchangeability for extreme-value and left-tail decreasing bivariate copulas. Scandinavian Journal of Statistics, 39(3), 480–496. https://doi.org/10.1111/j.1467-9469.2011.00772.x

Kojadinovic, I., & Yan, J. (2012). Goodness-of-fit testing based on a weighted bootstrap: A fast large-sample alternative to the parametric bootstrap. Canadian Journal of Statistics, 40(3), 480–500. https://doi.org/10.1002/cjs.11135

Wang, X., Sinha, A., Yan, J., & Chen, M.-H. (2012). Bayesian inference of interval-censored survival data. In D.-G. Chen, J. Sun, & K. E. Peace (Eds.), Interval-censored time-to-event data: Methods and applications (pp. 167–196). CRC Press. https://doi.org/10.1201/b12290-10

Yan, J., & Huang, J. (2012). Model selection for Cox models with time-varying coefficients. Biometrics, 68(2), 419–428. https://doi.org/10.1111/j.1541-0420.2011.01692.x

Yan, J., Liao, G.-Y., Gebremichael, M., Shedd, R., & Vallee, D. R. (2012). Characterizing the uncertainty in river stage forecasts conditional on point forecast values. Water Resources Research, 48(12), W12509. https://doi.org/10.1029/2012wr011818

Allignol, A., Latouche, A., Yan, J., & Fine, J. P. (2011). A regression model for the conditional probability of a competing event: Application to monoclonal gammopathy of unknown significance. Journal of the Royal Statistical Society, Series C: Applied Statistics, 60(1), 135–142. https://doi.org/10.1111/j.1467-9876.2010.00729.x

Gebremichael, M., Liao, G.-Y., & Yan, J. (2011). Nonparametric error model for high resolution satellite rainfall products. Water Resources Research, 47, W07504–W07513. https://doi.org/10.1029/2010WR009667

Genest, C., Kojadinovic, I., Nešlehová, J., & Yan, J. (2011). A goodness-of-fit test for bivariate extreme value copulas. Bernoulli, 17(1), 253–275. https://doi.org/10.3150/10-bej279

Guan, Y., Yan, J., & Sinha, R. (2011). Variance estimation for statistics computed from single recurrent event processes. Biometrics, 67(3), 711–718. https://doi.org/10.1111/j.1541-0420.2011.01559.x

Harel, O., Mukhopadhyay, N., & Yan, J. (2011). On a sequential probability ratio test subject to incomplete data. Sequential Analysis, 30, 441–456. https://doi.org/10.1080/07474946.2011.619103

Kojadinovic, I., Segers, J., & Yan, J. (2011). Large-sample tests of extreme-value dependence for multivariate copulas. Canadian Journal of Statistics, 39(4), 703–720. https://doi.org/10.1002/cjs.10110

Kojadinovic, I., & Yan, J. (2011). A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems. Statistics and Computing, 21(1), 17–30. https://doi.org/10.1007/s11222-009-9142-y

Kojadinovic, I., & Yan, J. (2011). Tests of serial independence for multivariate time series based on a Möbius decomposition of the independence empirical copula process. Annals of the Institute of Statistical Mathematics, 63(2), 347–373. https://doi.org/10.1007/s10463-009-0257-x

Kojadinovic, I., Yan, J., & Holmes, M. (2011). Fast large-sample goodness-of-fit for copulas. Statistica Sinica, 21(2), 841–871. https://doi.org/10.5705/ss.2011.037a

Prates, M. O., Dey, D. K., Willig, M. R., & Yan, J. (2011). Intervention analysis of hurricane effects on snail abundance in a tropical forest using long-term spatio-temporal data. Journal of Agricultural, Biological, and Ecological Statistics, 16(1), 142–156. https://doi.org/10.1007/s13253-010-0039-1

Shang, H., Yan, J., Gebremichael, M., & Ayalew, S. M. (2011). Trend analysis of extreme precipitation in the Northwestern Highlands of Ethiopia with a case study of Debre Markos. Hydrology and Earth System Sciences, 15(6), 1937–1944. https://doi.org/10.5194/hess-15-1937-2011

Shang, H., Yan, J., & Zhang, X. (2011). El Nin̈o–Southern Oscillation influence on winter maximum daily precipitation in California in a spatial model. Water Resources Research, 47, W11507–W11515. https://doi.org/10.1029/2011WR010415

Wang, X., & Yan, J. (2011). Fitting semiparametric regressions for panel count survival data with an R package spef. Computer Methods and Programs in Biomedicine, 104(2), 278–285. https://doi.org/10.1016/j.cmpb.2010.10.005

Kojadinovic, I., & Yan, J. (2010). Comparison of three semiparametric methods for estimating dependence parameters in copula models. Insurance: Mathematics and Economics, 47(1), 52–63. https://doi.org/10.1016/j.insmatheco.2010.03.008

Kojadinovic, I., & Yan, J. (2010). Modeling multivariate distributions with continuous margins using the copula R package. Journal of Statistical Software, 34(9), 1–20. https://doi.org/10.18637/jss.v034.i09

Kojadinovic, I., & Yan, J. (2010). Nonparametric rank-based tests of bivariate extreme-value dependence. Journal of Multivariate Analysis, 101(9), 2234–2249. https://doi.org/10.1016/j.jmva.2010.05.004

Wang, X., Gebremichael, M., & Yan, J. (2010). Weighted likelihood copula modeling of extreme rainfall events in connecticut. Journal of Hydrology, 390(1–2), 108–115. https://doi.org/10.1016/j.jhydrol.2010.06.039

Yan, J., & Academic ED SBIRT Research Collaborative. (2010). The impact of screening, brief intervention and referral for treatment in emergency department patients’ alcohol use: A 3-, 6- and 12-month follow-up. Alcohol & Alcoholism, 45(6), 514–519. https://doi.org/10.1093/alcalc/agq058

Yan, J., Cheng, Y., Fine, J. P., & Lai, H.-C. (2010). Uncovering symptom progression history from disease registry data with application to young cystic fibrosis patients. Biometrics, 66(2), 594–602. https://doi.org/10.1111/j.1541-0420.2009.01288.x

Cowles, M. K., Yan, J., & Smith, B. J. (2009). Reparameterized and marginalized posterior and predictive sampling for complex Bayesian geostatistical models. Journal of Computational and Graphical Statistics, 18(2), 262–282. https://doi.org/10.1198/jcgs.2009.08012

Yan, J., & Gebremichael, M. (2009). Estimating actual rainfall from satellite rainfall products. Atmospheric Research, 92(4), 481–488. https://doi.org/10.1016/j.atmosres.2009.02.004

Yan, J., & Huang, J. (2009). Partly functional temporal process regression with semiparametric profile estimating functions. Biometrics, 65(2), 431–440. https://doi.org/10.1111/j.1541-0420.2008.01071.x

Smith, B. J., Yan, J., & Cowles, M. K. (2008). Unified geostatistical modeling for data fusion and spatial heteroskedasticity with R package ramps. Journal of Statistical Software, 25(10), 1–21. https://doi.org/10.18637/jss.v025.i10

Yan, J., & Fine, J. P. (2008). Analysis of episodic data with application to recurrent pulmonary exacerbations in cystic fibrosis patients. Journal of the American Statistical Association, 103(482), 498–510. https://doi.org/10.1198/016214507000000482

Stramer, O., & Yan, J. (2007). Asymptotics of an efficient Monte Carlo estimation for the transition density of diffusion processes. Methodology & Computing in Applied Probability, 9(4), 483–496. https://doi.org/10.1007/s11009-006-9006-2

Stramer, O., & Yan, J. (2007). On simulated likelihood of discretely observed diffusion processes and comparison to closed-form approximation. Journal of Computational and Graphical Statistics, 16(3), 672–691. https://doi.org/10.1198/106186007x237306

Yan, J. (2007). Enjoy the joy of copulas: With a package copula. Journal of Statistical Software, 21(4), 1–21. https://doi.org/10.18637/jss.v021.i04

Yan, J. (2007). Spatial stochastic volatility for lattice data. Journal of Agricultural, Biological, and Environmental Statistics, 12(1), 25–40. https://doi.org/10.1198/108571107x178068

Yan, J., Cowles, M. K., Wang, S., & Armstrong, M. P. (2007). Parallelizing MCMC for Bayesian spatiotemporal geostatistical models. Statistics and Computing, 17(4), 323–335. https://doi.org/10.1007/s11222-007-9022-2

Yan, J., & Tamboli, C. P. (2007). Testing concordance of clinical characteristics in familial studies with application to inammatory bowel diseases. Biometrical Journal, 49(6), 840–853. https://doi.org/10.1002/bimj.200710383

Halekoh, U., Højsgaard, S., & Yan, J. (2006). The R package geepack for generalized estimating equations. Journal of Statistical Software, 15/2, 1–11. https://doi.org/10.18637/jss.v015.i02

Yan, J. (2006). Multivariate modeling with copulas and engineering applications. In H. Pham (Ed.), Handbook of engineering statistics (pp. 973–990). Springer. https://doi.org/10.1007/978-1-84628-288-1_51

Yan, J., & Fine, J. P. (2005). Functional association models for multivariate survival processes. Journal of the American Statistical Association, 100(469), 184–196. https://doi.org/10.1198/016214504000001286

Fine, J. P., Yan, J., & Kosorok, M. R. (2004). Temporal process regression. Biometrika, 91(3), 683–703. https://doi.org/10.1093/biomet/91.3.683

Yan, J., & Fine, J. (2004). Estimating equations for association structures (Pkg: P859-880). Statistics in Medicine, 23(6), 859–874. https://doi.org/10.1002/sim.1650

Yan, J., & Fine, J. (2004). Reply to comment on “Estimating equations for association structures” (Pkg: 859-880). Statistics in Medicine, 23(6), 879–880. https://doi.org/10.1002/sim.1736