Yan Sun Ph.D.

Publications | Book Chapters

• Sun, Y., Lin, C., (2017). Linear regression for interval-valued data in K_C(R): Linear Regression: Models, Analysis and Applications. Nova Science Publishers

An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.

Publications | Journal Articles

Academic Journal

• Duah, K., Sun, Y., Bean, B., (2025). Integrating multi-source geospatial information using Bayesian maximum entropy: A case study on design ground snow load prediction. Spatial Statistics, 67, 100894. doi: 10.1016/j.spasta.2025.100894
• Sun, Y., Rios, Z., Bean, B., (2024). Multi-sample means comparisons for imprecise interval data. International Journal of Approximate Reasoning, 176, 109322. doi: 10.1016/j.ijar.2024.109322
• Gaona-Partida, P., Yeh, C., Sun, Y., Cutler, A., (2024). Random forests regression for soft interval data. Communications in Statistics - Simulation and Computation, 1-20.
• Wagstaff, J., Bean, B., Wheeler, J., Maguire, M., Al-Rubaye, S., Sun, Y., (2023). Adaptive mapping of design ground snow loads in the conterminous United States. Journal of Structural Engineering, 150:1, doi: 10.1061/jsendh.steng-12396
• Geng, J., Jensen, G., Jackson, K., Pontsler, J., Rengarajan, V., Sun, Y., Britt, D.W, Huang, Y., (2022). Versatile activity and morphological effects of zinc oxide submicron particles as anticancer agents. Nanomedicine, 17:9, 627-644.
• Bean, B., Sun, Y., Maguire, M., (2022). Interval-valued Kriging models for geostatistical mapping with imprecise inputs. International Journal of Approximate Reasoning, 140, 31-51. doi: 10.1016/j.ijar.2021.10.003
• Sun, Y., Lian, G., Lu, Z., Loveland, J., Blackhurst, I., (2020). Modeling the Variance of Return Intervals Toward Volatility Prediction. Journal of Time Series Analysis, 41, 492-519.
• Plucinski, B., Sun, Y., Wang, S., Gillies, R.R, Eklund, J., Wang, C., (2019). Feasibility of Multi-Year Forecast for the Colorado River Water Supply: Time Series Modeling. Water, 11:2433
• McKinney, E., Chang, M., Maguire, M., Sun, Y., (2019). Prediction of Stress Increase at Ultimate in Unbonded Tendons using Sparse Principal Component Analysis. International Journal of Concrete Structures and Materials, 13:20
• Bean, B., Maguire, M., Sun, Y., (2019). Comparing design ground snow load prediction in Utah and Idaho. Journal of Cold Regions Engineering, 33:3, doi: 10.1061/(asce)cr.1943-5495.0000190
• Chang, M., Sun, Y., (2018). Stochastic modeling of bridge deterioration using classification tree and logistic regression. Journal of Infrastructure Systems, 25:1, 04018041.
• Sun, Y., Wang, S., Li, R., Buckley, B., Gillies, R.R, Hansen, K., (2018). Feasibility of predicting Vietnam’s autumn rainfall regime based on tree ring record and decadal variability. Climate, 6:42
• Bean, B., Sun, Y., (2017). Predicting Utah ground snow loads with PRISM. Journal of Structural Engineering, 143:9, doi: 10.1061/(asce)st.1943-541x.0001870
• Sun, Y., Bekker, M., DeRose, J., Kjelgren, R.K, Wang, S., (2017). Statistical treatment for the wet bias in tree-ring chronologies - a case study from the Interior West, USA. Environmental and Ecological Statistics, 24:1, 131-150.
• Maguire, M., Chang, M., Collins, W.N, Sun, Y., (2017). Stress increase of unbonded tendons in continuous post-tensioned members. Journal of Bridge Engineering, 22:2, 04016115.
• Chang, M., Maguire, M., Sun, Y., (2017). Framework for Mitigating Human Bias in Selection of Explanatory Variables for Bridge Deterioration Modeling. ASCE Journal of Infrastructure Systems, 23:3, 04017002.
• Gao, R., Sun, Y., Ralescu, D., (2017). Order statistics of uncertain random variables with application to k-out-of-n system. Fuzzy Optimization and Decision Making, 16:2, 159-181.
• Sun, Y., (2017). Asymptotic tests for interval-valued means. Statistics and Probability Letters, 121, 70-77.
• Sun, Y., (2016). Linear regression with interval-valued data. WIREs Computational Statistics, 8, 54-60.
• Sang, H., Sun, Y., (2015). Simultaneous sparse model selection and coefficient estimation for heavy-tailed autoregressive processes. Statistics, 49:1, 187-208.
• Gillies, R.R, Chung, O., DeRose, J., Wang, S., Sun, Y., (2015). Added value from 576 years of tree-ring records in the prediction of the Great Salt Lake level. Journal of Hydrology, 529, 962-968.
• Sun, Y., Ralescu, D., (2015). A normal hierarchical model and minimum contrast estimation for random intervals. Annals of the Institute of Statistical Mathematics, 67, 313-333.
• Gillies, R.R, Wang, S., Sun, Y., Chung, O., (2013). Supportive empirical modeling for the forecast of monsoon precipitation in Nepal. International Journal of Climatology, 33, 3047-3054.
• Sun, Y., (2012). Modeling long-memory time series with sparse autoregressive processes. Journal of Uncertain Systems, 6:4, 289-298.
• Sun, Y., Lin, X., (2012). Regularization for stationary multivariate time series. Quantitative Finance, 12:4, 573-586.

An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.

Publications | Technical Reports

Other Reports

• Bean, B., Sun, Y., Kwag, J., Al-Rubaye, S., Wheeler, J., Jarman, S., Rogers, M., (2021). The 2020 National Snow Load Study. Utah State University Digital Commons *

An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.

Publications | Other

Magazine/Trade Publications

An asterisk (*) at the end of a publication indicates that it has not been peer-reviewed.