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Author Title Type Year(Asc)
2024
Nadi, A. Ahmadi, Steiner, S. H., & Stevens, N. T.. (2024). Assessing Measurement System Agreement in the Presence of Reproducibility and Repeatability. Technometrics. Retrieved from https://doi.org/10.1080/00401706.2023.2296465
Pan, R., Lu, L., & Stevens, N.. (2024). Editorial for reliability engineering special issue. Quality Engineering. Retrieved from https://doi.org/10.1080/08982112.2024.2293610
Rigdon, S. E., Stevens, N. T., Wilson, J. D., & Woodall, W. H.. (2024). First to signal criterion for comparing control chart performance. Quality Engineering, 36(2), 427-438. Retrieved from https://doi.org/10.1080/08982112.2023.2223690
2023
Sen, A., Stevens, N. T., N Tran, K., Agarwal, R. R., Zhang, Q., & Dubin, J. A.. (2023). Forecasting Daily COVID-19 Cases with Gradient Boosted Trees and Other Methods: Evidence from U.S. Cities. Frontiers in Public Health, in press.
Larsen, N., Stallrich, J. W., Sengupta, S., Deng, A., Kohavi, R., & Stevens, N. T.. (2023). Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology. The American Statistician, in press.
Smucker, B. J., Stevens, N. T., Asscher, J., & Goos, P.. (2023). Profiles in the Teaching of Experimental Design and Analysis. Journal of Statistics and Data Science Education, 31(3), 211—224.
Bui, T., Steiner, S. H., & Stevens, N. T.. (2023). General Additive Network Effect Models. The New Journal of Statistics in Data Science, 1(3), 342–360.
Stevens, N. T.. (2023). Is designed data collection still relevant in the big data era? A discussion. Quality and Reliability Engineering, 39(4), 1107–1109.
2022
Cook, C. M. Anderso, Lu, L., Brenneman, W., de Mast, J., Faltin, F., Freeman, L., Guthrie, W., et al. (2022). Statistical Engineering – Part 2: Future. Quality Engineering, 34(4), 446–467.
Anderson-Cook, C. M., Lu, L., Brenneman, W., de Mast, J., Faltin, F., Freeman, L., Guthrie, W., et al. (2022). Statistical Engineering – Part 1: Past and Present. Quality Engineering, 34(4), 426–445.
Lu, L., Anderson-Cook, C. M., Stevens, N. T., & Hagar, L.. (2022). Using a Baseline with the Probability of Agreement to Compare Distribution Characteristics. Quality Engineering, 34(3), 322–343.
Stevens, N. T., & Hagar, L.. (2022). Comparative probability metrics: Using posterior probabilities to account for practical equivalence in A/B tests. The American Statistician, 76(3), 224-237.
Stevens, N. T., Sen, A., Kiwon, F., Morita, P. P., Steiner, S. H., & Zhang, Q.. (2022). Estimating the effects of non-pharmaceutical intervent and population mobility on daily COVID-19 cases: Evidence from Ontario. Canadian Public Policy, 48(1), 144–161.
Yu, L., Zwetsloot, I. M., Stevens, N. T., Wilson, J. D., & Tsui, K. L.. (2022). Monitoring dynamic networks: a simulation-based strategy for comparing monitoring methods and a comparative study. Quality and Reliability Engineering International, 38(3), 1226–1250.
2021
Stevens, N. T., & Wilson, J. D.. (2021). The past, present, and future of network monitoring: A panel discussion. Quality Engineering, 33(4), 715–718.
Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., Paynabar, K., et al. (2021). Foundations of network monitoring: Definitions and applications. Quality Engineering, 33(4), 719–730.
Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Paris, C., Paynabar, K., Perry, M. B., et al. (2021). The interdisciplinary nature of network monitoring: Advantages and disadvantages. Quality Engineering, 33(4), 731–735.
Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., Paynabar, K., et al. (2021). Research in network monitoring: Connections with SPM and new directions. Quality Engineering, 33(4), 736–748.
Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., Paynabar, K., et al. (2021). Broader impacts of network monitoring: Its role in government, industry, technology, and beyond. Quality Engineering, 33(4), 749–757.
Motalebi, N., Stevens, N. T., & Steiner, S. H.. (2021). Hurdle blockmodels for sparse network modeling. The American Statistician, 75(4), 383–393.

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