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Author Title Type [ Year(Desc)]
2020
Stevens, N. T. . (2020). Discussion of “Statistics= Analytics?”. Quality Engineering, 32(2), 145-148.
Parker, R. A. , Scott, C. , Inacio, V. , & Stevens, N. T. . (2020). Using multiple agreement methods for continuous repeated measures data: a tutorial for practitioners. BMC Medical Research Methodology, 20(1), 1-14.
Stevens, N. T. , Lu, L. , Anderson-Cook, C. M. , & Rigdon, S. . (2020). Bayesian probability of agreement for comparing survival or reliability functions with parametric lifetime regression models. Quality Engineering, 32(3), 312-332.
Stevens, N. T. , Rigdon, S. E. , & Anderson-Cook, C. M. . (2020). Bayesian probability of agreement for comparing the similarity of response surfaces. Journal of Quality Technology, 52(1), 67-80.
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.
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.
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.

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