| A storage model approach to the assessment of snow depth trends J Woody, R Lund, AJ Grundstein, TL Mote Water resources research 45 (10), 2009 | 13 | 2009 |
| Testing for seasonal means in time series data G Liu, Q Shao, R Lund, J Woody Environmetrics 27 (4), 198-211, 2016 | 12 | 2016 |
| Trend assessment for daily snow depths with changepoint considerations J Lee, R Lund, J Woody, Y Xu Environmetrics 31 (1), e2580, 2020 | 9 | 2020 |
| Time series regression with persistent level shifts J Woody Statistics & Probability Letters 102, 22-29, 2015 | 6 | 2015 |
| Statistical methods for forecasting daily snow depths and assessing trends in inter-annual snow depth dynamics J Woody, QQ Lu, J Livsey Environmental and Ecological Statistics 27, 609-628, 2020 | 4 | 2020 |
| A linear regression model with persistent level shifts: An alternative to infill asymptotics J Woody, R Lund Statistics & Probability Letters 95, 118-124, 2014 | 4 | 2014 |
| Tuning Extreme NEXRAD and CMORPH Precipitation Estimates J Woody, R Lund, M Gebremichael Journal of Hydrometeorology 15 (3), 1070-1077, 2014 | 4 | 2014 |
| Application of multivariate storage model to quantify trends in seasonally frozen soil J Woody, Y Wang, J Dyer Open Geosciences 8 (1), 310-322, 2016 | 3 | 2016 |
| A Statistical Analysis of Daily Snow Depth Trends in North America J Woody, Y Xu, J Dyer, R Lund, AP Hewaarachchi Atmosphere 12 (7), 820, 2021 | 2 | 2021 |
| A forensic statistical analysis of fraud in the federal food stamp program J Woody, Z Zhao, R Lund, TL Wu Annals of Applied Statistics, 2024 | | 2024 |
| Trends in Northern Hemispheric Snow Presence Y Jia, R Lund, J Kong, J Dyer, J Woody, JS Marron Journal of Hydrometeorology 24 (6), 1137-1154, 2023 | | 2023 |
| Lindley Processes with Correlated Changes J Grant | | 2022 |
| Dataset for: Trend Assessment for Daily Snow Depths with Changepoint Considerations R Lund, J Woody, J Lee, Y Xu figshare Academic Research System, 2019 | | 2019 |
| Some New Problems in Changepoint Analysis J Woody | | 2009 |
