Authors: Stephens, G. K., Sitnov, M. I., Weigel, R. S., Turner, D. L., Tsyganenko, N. A., Rogers, A. J., Genestreti, K. J., Slavin, J. A.

Subject Terms: substorms and storms, X-line, thin current sheet, data-mining, magnetotail, reconnection, Astronomy and Astrophysics, Science

File Description: application/pdf

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Availability: https://doi.org/10.1029/2022JA03106610.1029/JZ070i017p0421910.5281/zenodo.686282910.1016/0032-0633(91)90058-I10.1029/94JA0319310.1029/98JA0229210.1029/2001JA00021910.1029/2001JA00022010.5194/angeo-31-1745-201310.1002/2013JA01934610.1029/2004RG00016110.1038/nphys34210.1029/2018SW00206110.1016/0032-0633(81)90141-010.1103/PhysRevLett.6.4710.1029/2012JA01768310.1029/JA093iA08p0858310.1029/GM030p017810.1111/j.1365-246X.1972.tb06115.x10.3847/0004-637x/818/2/148
https://hdl.handle.net/2027.42/175895

Authors: Nam, SunJin, Lonn, Steven, Brown, Thomas, Davis, Cinda-Sue, Koch, Darryl

Contributors: Library, University of Michigan, LED Lab, University of Michigan, School of Information, University of MIchigan, Center for Statistical Consultation and Research, University of Michigan, Ann Arbor

Subject Terms: Learning Analytics, Data Analysis, Course Advising, Data Mining, Information and Library Science, Social Sciences

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Relation: https://hdl.handle.net/2027.42/110782

Availability: https://doi.org/10.1145/2567574.2567589
https://hdl.handle.net/2027.42/110782

Authors: Azari, Abigail R., Liemohn, Michael W., Jia, Xianzhe, Thomsen, Michelle F., Mitchell, Donald G., Sergis, Nick, Rymer, Abigail M., Hospodarsky, George B., Paranicas, Christopher, Vandegriff, Jon

Subject Terms: data mining, energetic particles, Saturn’s magnetosphere, interchange injections, plasma transport, Astronomy and Astrophysics, Science

File Description: application/pdf

Relation: Azari, Abigail R.; Liemohn, Michael W.; Jia, Xianzhe; Thomsen, Michelle F.; Mitchell, Donald G.; Sergis, Nick; Rymer, Abigail M.; Hospodarsky, George B.; Paranicas, Christopher; Vandegriff, Jon (2018). "Interchange Injections at Saturn: Statistical Survey of Energetic H+ Sudden Flux Intensifications." Journal of Geophysical Research: Space Physics 123(6): 4692-4711.; https://hdl.handle.net/2027.42/145315; Journal of Geophysical Research: Space Physics; Paranicas, C., Thomsen, M. F., Achilleos, N., Andriopoulou, M., Badman, S. V., Hospodarsky, G., et al. ( 2016 ). Effects of radial motion on interchange injections at Saturn. Icarus, 264, 342 – 351.; Krimigis, S. M., Sergis, N., Mitchell, D. G., Hamilton, D. C., & Krupp, N. ( 2007 ). A dynamic, rotating ring current around Saturn. Nature, 450 ( 7172 ), 1050 – 1053. Retrieved from doi: https://doi.org/10.1038/nature06425; Krzywinski, M. I., Schein, J. E., Birol, I., Connors, J., Gascoyne, R., Horsman, D., et al. ( 2009 ). 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Availability: https://doi.org/10.1029/2018JA025391
https://doi.org/10.1101/gr.092759.109
https://doi.org/10.1007/978‐1‐4020‐9217‐6_11
https://doi.org/10.1029/2005GL022485
https://doi.org/10.1002/9781118842324.ch19
https://doi.org/10.1029/2009JA015122
https://doi.org/10.1029/2006GL028676
https://doi.org/10.1002/2016JA023056
https://doi.org/10.1002/swe.20056
https://doi.org/10.1016/j.pss.2009.04.010

Authors: Goldsmith, Bryan R., Esterhuizen, Jacques, Liu, Jin‐xun, Bartel, Christopher J., Sutton, Christopher

Subject Terms: compressed sensing, machine learning, heterogeneous catalysis, computational catalysis, data mining, Chemical Engineering, Engineering, Science

File Description: application/pdf

Relation: Goldsmith, Bryan R.; Esterhuizen, Jacques; Liu, Jin‐xun; Bartel, Christopher J.; Sutton, Christopher (2018). "Machine learning for heterogeneous catalyst design and discovery." AIChE Journal 64(7): 2311-2323.; http://hdl.handle.net/2027.42/144583; AIChE Journal; Natarajan SK, Behler J. Neural network molecular dynamics simulations of solidâ liquid interfaces: water at lowâ index copper surfaces. Phys Chem Chem Phys. 2016; 18 ( 41 ): 28704.; Yao K, Herr JE, Toth DW, Mckintyre R, Parkhill J. The TensorMolâ 0.1 model chemistry: a neural network augmented with longâ range physics. Chem Sci. 2018; 9 ( 8 ): 2261.; Campbell CT. The degree of rate control: a powerful tool for catalysis research. ACS Catal. 2017; 7 ( 4 ): 2770.; Hratchian HP, Schlegel HB. Finding minima, transition states, and following reaction pathways on ab initio potential energy surfaces. 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Availability: https://doi.org/10.1002/aic.16198
http://hdl.handle.net/2027.42/144583

Authors: McRoberts, D. Brent, Quiring, Steven M., Guikema, Seth D.

Subject Terms: outage prediction, reliability, storm response planning, outage model, hurricanes, Data mining, Business (General), Business and Economics

File Description: application/pdf

Relation: McRoberts, D. Brent; Quiring, Steven M.; Guikema, Seth D. (2018). "Improving Hurricane Power Outage Prediction Models Through the Inclusion of Local Environmental Factors." Risk Analysis 38(12): 2722-2737.; http://hdl.handle.net/2027.42/147200; Risk Analysis; Dobos R, Sinclair H, Hipple, K. User Guide National Commodity Crop Productivity Index (NCCPI) Version 1.0. U.S. Department of Agriculture, Natural Resources Conservation Service, 2012.; Davidson RA, Haibin L, Sarpong IK, Sparks P, Rosowsky DV. Electric power distribution system performance in Carolina hurricanes. Natural Hazards Review, 2003; 8: 36 – 45.; Homer CG, Dewitz JA, Yang L, Jin S, Danielson P, Xian G, Coulston J, Herold ND, Wickham JD, Megown K. Completion of the 2011 National Land Cover Database for the Conterminous United States—Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, 2015; 81 ( 5 ): 345 – 354.; Anderson JR. 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Availability: https://doi.org/10.1111/risa.12728
http://hdl.handle.net/2027.42/147200

Authors: Cordell, Sigrid Anderson, Gomis, Melissa

Contributors: Library, University of Michigan, Doane University, Ann Arbor

Subject Terms: data mining, library services, supporting DH across the institution, teaching DH, Information and Library Science, Social Sciences

File Description: application/pdf

Relation: https://hdl.handle.net/2027.42/139596; https://doi.org/10.1080/10691316.2017.1338979; College & Undergraduate Libraries; orcid:0000-0003-3956-0606; orcid:0000-0002-5622-8560; Cordell, Sigrid; 0000-0003-3956-0606; Gomis, Melissa; 0000-0002-5622-8560

Availability: https://doi.org/10.1080/10691316.2017.1338979
https://hdl.handle.net/2027.42/139596

Authors: Linden, Ariel, Yarnold, Paul R.

Subject Terms: quasi‐experimental, structural breaks, causal inference, data mining, interrupted time series analysis, machine learning, maximum‐accuracy model, optimal discriminant analysis, Medicine (General), Health Sciences

File Description: application/pdf

Relation: Linden, Ariel; Yarnold, Paul R. (2016). "Using machine learning to identify structural breaks in single‐group interrupted time series designs." Journal of Evaluation in Clinical Practice 22(6): 851-855.; https://hdl.handle.net/2027.42/135143; Journal of Evaluation in Clinical Practice; Abadie, A., Diamond, A. & Hainmueller, J. ( 2010 ) Synthetic control methods for comparative case studies: estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105, 493 – 505.; Riley, W. T., Glasgow, R. E., Etheredge, L. & Abernethy, A. P. ( 2013 ) Rapid, responsive, relevant (R3) research: a call for a rapid learning health research enterprise. Clinical and Translational Medicine, 2, 1 – 6.; Effective Practice and Organisation of Care (EPOC). ( 2015 ) Interrupted Time Series (ITS) Analyses. EPOC Resources for review authors. Oslo: Norwegian Knowledge Centre for the Health Services. 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Washington, DC: APA Books.; Yarnold, P.R., & Soltysik, R.C. ( 2016 ) Maximizing Predictive Accuracy. Chicago, IL: ODA Books. DOI:10.13140/RG.2.1.1368.3286; Breslow, L. & Johnson, M. ( 1993 ) California’s Proposition 99 on Tobacco, and Its Impact. Annual Review of Public Health, 14, 585 – 604.; Abadie, A., Diamond, A., & Hainmueller, J. ( 2014 ) SYNTH: Stata module to implement synthetic control methods for comparative case studies. Statistical Software Components S457334, Department of Economics, Boston College. https://ideas.repec.org/c/boc/bocode/s457334.html; Orzechowski, W. & Walker, R. C. ( 2005 ) The Tax Burden on Tobacco. Historical Compilation vol. 40. Arlington, VA: Orzechowski & Walker.; Grimm, L. G. & Yarnold, P. R. (eds) ( 1995 ) Reading and Understanding Multivariate Statistics. Washington, D.C.: APA Books.; Grimm, L. G. & Yarnold, P. R. (eds) ( 2000 ) Reading and Understanding More Multivariate Statistics. Washington, D.C.: APA Books.; Witten, I. H., Frank, E. & Hall, M. A. ( 2011 ) Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. San Francisco: Morgan Kaufmann.; Linden, A., Adams, J. & Roberts, N. ( 2005 ) Evaluating disease management program effectiveness: an introduction to the bootstrap technique. Disease Management and Health Outcomes, 13, 159 – 167.; Linden, A., Adams, J. & Roberts, N. ( 2006 ) Strengthening the case for disease management effectiveness: unhiding the hidden bias. Journal of Evaluation in Clinical Practice, 12, 140 – 147.; Andrews, D. W. K. ( 1993 ) Tests for parameter instability and structural change with unknown change point. Econometrica, 61, 821 – 856.; Yarnold, P. R. ( 2013 ) Percent oil‐based energy consumption and average percent GDP growth: a small sample UniODA analysis. Optimal Data Analysis, 2, 60 – 61.; Yarnold, P. R. ( 2015 ) UniODA vs. McNemar’s test: a small sample analysis. Optimal Data Analysis, 4, 27 – 28.; Linden, A. & Adams, J. L. ( 2011 ) Applying a propensity‐score based weighting model to interrupted time series data: improving causal inference in program evaluation. Journal of Evaluation in Clinical Practice, 17, 1231 – 1238.; Linden, A. ( 2015a ) Conducting interrupted time‐series analysis for single‐ and multiple‐group comparisons. The Stata Journal, 15, 480 – 500.; Campbell, D. T. & Stanley, J. C. ( 1966 ) Experimental and Quasi‐Experimental Designs for Research. Chicago, IL: Rand McNally.; Shadish, W. R., Cook, T. D. & Campbell, D. T. ( 2002 ) Experimental and Quasi‐Experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin.; Linden, A. ( 2007 ) Estimating the effect of regression to the mean in health management programs. Disease Management and Health Outcomes, 15, 7 – 12.; Linden, A. ( 2013 ) Assessing regression to the mean effects in health care initiatives. BMC Medical Research Methodology, 13, 1 – 7.; Linden, A., Adams, J. & Roberts, N. ( 2004 ) The generalizability of disease management program results: getting from here to there. Managed Care Interface, 17, 38 – 45.; Biglan, A., Ary, D. & Wagenaar, A. C. ( 2000 ) The value of interrupted time‐series experiments for community intervention research. Prevention Science, 1, 31 – 49.; Gillings, D., Makuc, D. & Siegel, E. ( 1981 ) Analysis of interrupted time series mortality trends: an example to evaluate regionalized perinatal care. American Journal of Public Health, 71, 38 – 46.; Muller, A. ( 2004 ) Florida’s motorcycle helmet law repeal and fatality rates. American Journal of Public Health, 94, 556 – 558.; Briesacher, B. A., Soumerai, S. B., Zhang, F., Toh, S., Andrade, S. E., Wagner, J. L., Shoaibi, A. & Gurwitz, J. H. ( 2013 ) A critical review of methods to evaluate the impact of FDA regulatory actions. Pharmacoepidemiology and Drug Safety, 22, 986 – 994.; Ramsay, C. R., Matowe, L., Grilli, R., Grimshaw, J. M. & Thomas, R. E. 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Availability: https://doi.org/10.1111/jep.12544
https://hdl.handle.net/2027.42/135143

Authors: Linden, Ariel, Yarnold, Paul R.

Subject Terms: observational studies, observed characteristics, selection bias, selection, machine learning, data mining, Medicine (General), Health Sciences

File Description: application/pdf

Relation: Linden, Ariel; Yarnold, Paul R. (2016). "Using data mining techniques to characterize participation in observational studies." Journal of Evaluation in Clinical Practice 22(6): 835-843.; http://hdl.handle.net/2027.42/134951; Journal of Evaluation in Clinical Practice; Feinglass, J., Yarnold, P. R., Martin, G. J. & McCarthy, W. J. ( 1998 ) A classification tree analysis of selection for discretionary treatment. Medical Care, 36, 740 – 747.; Yarnold, P. R. & Soltysik, R. C. ( 2016 ) Maximizing Predictive Accuracy. Chicago, IL: ODA Books. doi:10.13140/RG.2.1.1368.3286.; Linden, A., Adams, J. & Roberts, N. ( 2003 ) An assessment of the total population approach for evaluating disease management program effectiveness. Disease Management, 6, 93 – 102.; Linden, A. ( 2011 ) Identifying spin in health management evaluations. Journal of Evaluation in Clinical Practice, 17, 1223 – 1230.; Linden, A. & Samuels, S. J. ( 2013 ) Using balance statistics to determine the optimal number of controls in matching studies. Journal of Evaluation in Clinical Practice, 19, 968 – 975.; Fernández‐Delgado, M., Cernadas, E., Barro, S. & Amorim, D. ( 2014 ) Do we need hundreds of classifiers to solve real world classification problems? The Journal of Machine Learning Research, 15, 3133 – 3181.; Noble, W. S. ( 2006 ) What is a support vector machine? Nature Biotechnology, 24, 1565 – 1567.; Breiman, L. ( 2001 ) Random forests. Machine Learning, 45, 5 – 32.; Witten, I. H., Frank, E. & Hall, M. A. ( 2011 ) Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. San Francisco, CA: Morgan Kaufmann.; Breiman, L., Friedman, J., Olshen, R. & Stone, C. ( 1984 ) Classification and Regression Trees. Belmont, CA: Wadsworth International Group.; Quinlan, J. R. ( 1993 ) C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.; Yarnold, P. R., Soltysik, R. C. & Bennett, C. L. 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Availability: https://doi.org/10.1111/jep.12515
http://hdl.handle.net/2027.42/134951

Authors: Linden, Ariel, Yarnold, Paul R.

Subject Terms: selection bias, data mining, matching studies, machine‐learning, covariate balance, observed characteristics, Medicine (General), Health Sciences

File Description: application/pdf

Relation: Linden, Ariel; Yarnold, Paul R. (2016). "Using machine learning to assess covariate balance in matching studies." Journal of Evaluation in Clinical Practice 22(6): 844-850.; http://hdl.handle.net/2027.42/135124; Journal of Evaluation in Clinical Practice; Witten, I. H., Frank, E. & Hall, M. A. ( 2011 ) Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. San Francisco: Morgan Kaufmann.; Yarnold, P. R. & Soltysik, R. C. ( 1991 ) Theoretical distributions of optima for univariate discrimination of random data. Decision Sciences, 22, 739 – 752.; Carmony, L., Yarnold, P. R. & Naeymi‐Rad, F. ( 1997 ) One‐tailed type I error rates for balanced two‐category UniODA with a random ordered attribute. Annals of Operations Research, 74, 223 – 238.; Flury, B. K. & Reidwyl, H. ( 1986 ) Standard distance in univariate and multivariate analysis. The American Statistician, 40, 249 – 251.; Austin, P. C. 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( 2010a ) Using propensity score‐based weighting in the evaluation of health management programme effectiveness. Journal of Evaluation in Clinical Practice, 16, 175 – 179.; Linden, A. & Adams, J. L. ( 2010b ) Evaluating health management programmes over time. Application of propensity score‐based weighting to longitudinal data. Journal of Evaluation in Clinical Practice, 16, 180 – 185.; Linden, A. & Adams, J. L. ( 2011 ) Applying a propensity‐ score based weighting model to interrupted time series data: improving causal inference in program evaluation. Journal of Evaluation in Clinical Practice, 17, 1231 – 1238.; Linden, A. & Adams, J. L. ( 2012 ) Combining the regression‐discontinuity design and propensity‐score based weighting to improve causal inference in program evaluation. Journal of Evaluation in Clinical Practice, 18, 317 – 325.; Linden, A. ( 2014 ) Combining propensity score‐based stratification and weighting to improve causal inference in the evaluation of health care interventions. Journal of Evaluation in Clinical Practice, 20, 1065 – 1071.; Linden, A., Adams, J. & Roberts, N. ( 2005 ) Evaluating disease management program effectiveness: an introduction to the bootstrap technique. Disease Management and Health Outcomes, 13, 159 – 167.; Linden, A., Adams, J. & Roberts, N. ( 2004 ) The generalizability of disease management program results: getting from here to there. Managed Care Interface, 17, 38 – 45.; Yarnold, P. R. ( 2010 ) GenUniODA vs. log‐linear model: modeling discrimination in organizations. Optimal Data Analysis, 1, 59 – 61.; Yarnold, P. R. ( 1996 ) Discriminating geriatric and non‐geriatric patients using functional status information: an example of classification tree analysis via UniODA. Educational and Psychological Measurement, 56, 656 – 667.; Yarnold, P. R. & Bryant, F. B. ( 2015a ) Obtaining a hierarchically optimal CTA model via UniODA software. Optimal Data Analysis, 4, 36 – 53.; Yarnold, P. R. & Bryant, F. B. ( 2015b ) Obtaining an enumerated CTA model via automated CTA software. Optimal Data Analysis, 4, 54 – 60.; Athey, S. & Imbens, G. ( 2015 ) Machine learning methods for estimating heterogeneous causal effects. Working Paper. Downloadable from http://arxiv.org/abs/1504.01132 [Accessed on 30 November 2015].; Linden, A. & Adams, J. ( 2006 ) Evaluating disease management program effectiveness: an introduction to instrumental variables. Journal of Evaluation in Clinical Practice, 12, 148 – 154.; Linden, A., Adams, J. & Roberts, N. ( 2004 ) Evaluating disease management program effectiveness: an introduction to survival analysis. Disease Management, 7, 180 – 190.; Linden, A., Adams, J. & Roberts, N. ( 2006 ) Strengthening the case for disease management effectiveness: unhiding the hidden bias. 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( 2016 ) Maximizing Predictive Accuracy. Chicago, IL: ODA Books. DOI:10.13140/RG.2.1.1368.3286; Linden, A., Adams, J. & Roberts, N. ( 2003 ) An assessment of the total population approach for evaluating disease management program effectiveness. Disease Management, 6, 93 – 102.; Linden, A. & Yarnold, P. R. (In Print) Using data mining techniques to characterize participation in observational studies. Journal of Evaluation in Clinical Practice, 22, 835 – 843.; Linden, A. ( 2006 ) Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis. Journal of Evaluation in Clinical Practice, 12, 132 – 139.; Linden, A. ( 2011 ) Identifying spin in health management evaluations. Journal of Evaluation in Clinical Practice, 17, 1223 – 1230.; Rosenbaum, P. R. & Rubin, D. B. ( 1983 ) The central role of propensity score in observational studies for causal effects. Biometrika, 70, 41 – 55.; Rosenbaum, P. R. ( 1989 ) Optimal matching for observational studies. Journal of the American Statistical Association, 84, 1024 – 1302.

Availability: https://doi.org/10.1111/jep.12538
http://hdl.handle.net/2027.42/135124

Authors: Linden, Ariel, Yarnold, Paul R, Nallamothu, Brahmajee K

Subject Terms: efficacy, machine learning, dose–response, data mining, adherence, Medicine (General), Health Sciences

File Description: application/pdf

Relation: Linden, Ariel; Yarnold, Paul R; Nallamothu, Brahmajee K (2016). "Using machine learning to model dose–response relationships." Journal of Evaluation in Clinical Practice 22(6): 856-863.; http://hdl.handle.net/2027.42/134965; Journal of Evaluation in Clinical Practice; Newson, R. B. ( 2010 ) Frequentist q‐values for multiple‐test procedures. The Stata Journal, 10, 568 – 584.; Eisenbeis, R. A. ( 1977 ) Pitfalls in the application of discriminant analysis in business, finance, and economics. The Journal of Finance, 32, 875 – 900.; Nishikawa, K., Kubota, Y. & Ooi, T. ( 1983 ) Classification of proteins into groups based on amino acid composition and other characters, II: grouping into four types. Journal of Biochemistry, 94, 997 – 1007.; Yarnold, P. R. ( 1992 ) Statistical analysis for single‐case designs. In Social Psychological Applications to Social Issues: Vol. 2. Methodological Issues in Applied Social Research (eds F. B. Bryant, L. Heath, E. Posavac, J. Edwards, S. Tindale, E. Henderson & Y. Suarez‐Balcazar ), pp. 177 – 197. New York: Plenum.; Linden, A., & Yarnold, P.R. ( 2016 ) Using data mining techniques to characterize participation in observational studies. Journal of Evaluation in Clinical Practice, 22, 835 – 843.; Linden, A., Adams, J. & Roberts, N. ( 2004 ) The generalizability of disease management program results: getting from here to there. Managed Care Interface, 17, 38 – 45.; Witten, I. H., Frank, E. & Hall, M. A. ( 2011 ) Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. San Francisco: Morgan Kaufmann.; Linden, A., Adams, J. & Roberts, N. ( 2005 ) Evaluating disease management program effectiveness: an introduction to the bootstrap technique. Disease Management and Health Outcomes, 13, 159 – 167.; Huber, P. J. ( 1967 ) The behavior of maximum likelihood estimates under nonstandard conditions. In Vol. 1 of Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 221 – 233. 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Kulikowski ), 48, Suppl 1, pp. 121 – 133.; Couto, J., Webster, L., Romney, M., Leider, H. & Linden, A. ( 2009 ) Using an algorithm applied to urine drug screening to assess adherence to an OxyContin regimen. Journal of Opioid Management, 5, 359 – 364.; Couto, J., Webster, L., Romney, M., Leider, H. & Linden, A. ( 2011 ) Use of an algorithm applied to urine drug screening to assess adherence to a hydrocodone regimen. Journal of Clinical Pharmacology & Therapeutics, 36, 200 – 207.; Linden, A. & Adams, J. L. ( 2010a ) Using propensity score‐based weighting in the evaluation of health management programme effectiveness. Journal of Evaluation in Clinical Practice, 16, 175 – 179.; Linden, A. & Adams, J. L. ( 2010b ) Evaluating health management programmes over time. Application of propensity score‐based weighting to longitudinal data. Journal of Evaluation in Clinical Practice, 16, 180 – 185.; Linden, A. & Adams, J. L. ( 2011 ) Applying a propensity‐score based weighting model to interrupted time series data: improving causal inference in program evaluation. Journal of Evaluation in Clinical Practice, 17, 1231 – 1238.; Linden, A. & Adams, J. L. ( 2012 ) Combining the regression‐discontinuity design and propensity‐score based weighting to improve causal inference in program evaluation. Journal of Evaluation in Clinical Practice, 18, 317 – 325.; Linden, A. ( 2014 ) Combining propensity score‐based stratification and weighting to improve causal inference in the evaluation of health care interventions. Journal of Evaluation in Clinical Practice, 20, 1065 – 1071.; Linden, A., Uysal, S. D., Ryan, A. & Adams, J. L. ( 2016 ) Estimating causal effects for multivalued treatments: a comparison of approaches. Statistics in Medicine, 35, 534 – 552.; Peck, C. C., Barr, W. H., Benet, L. Z., et al. ( 1992 ) Opportunities for integration of pharmacokinetics, pharmacodynamics, and toxicokinetics in rational drug development. Journal of Pharmaceutical Sciences, 81, 605 – 610.; Royston, P. ( 2014 ) A smooth covariate rank transformation for use in regression models with a sigmoid dose–response function. Stata Journal, 14, 329 – 341.; Di Veroli, G. Y., Fornari, C., Goldlust, I., Mills, G., Koh, S. B., Bramhall, J. L., Richards, F. M. & Jodrell, D. I. ( 2015 ) An automated fitting procedure and software for dose–response curves with multiphasic features. Scientific reports, 5, 1 – 11.; Yarnold, P. R. & Soltysik, R. C. ( 2005 ) Optimal Data Analysis: A Guidebook with Software for Windows. Washington, DC: APA Books.; Yarnold, P. R. & Soltysik, R. C. ( 2016 ) Maximizing Predictive Accuracy. Chicago, IL: ODA Books. doi:10.13140/RG.2.1.1368.3286; Linden, A. & Yarnold, P. R. ( 2016 ) Using machine learning to assess covariate balance in matching studies. 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( 2003 ) Evaluating disease management program effectiveness: an introduction to time series analysis. Disease Management, 6, 243 – 255.; Feinstein, A. R. ( 1988 ) Statistical significance versus clinical importance. Quality of Life and Cardiovascular Care, 4, 99 – 102.; Kraemer, H. C. ( 1992 ) Evaluating Medical Tests. Newbury Park, CA: Sage.; Baus, J. W. & Gose, E. E. ( 1995 ) Leukocyte pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics, SMC‐2, 513 – 526.

Availability: https://doi.org/10.1111/jep.12573
http://hdl.handle.net/2027.42/134965

Authors: Krumm, Andrew E., Waddington, Richard Joseph, Teasley, Stephanie D., Lonn, Steven

Contributors: School of Information, University of Michigan, USE Lab, Digital Media Commons, University of Michigan, Center for Technology in Learning, SRI International , Menlo Park , CA , USA, Institute for Educational Initiatives, University of Notre Dame , Notre Dame , IN , USA, Ann Arbor

Subject Terms: Learning Analytics, Assessment, Testing and Evaluation, Data Mining and Knowledge Discovery, Information and Library Science, Social Sciences

File Description: application/pdf

Relation: In (J. A. Larusson & B. White, Eds.) Learning Analytics: From Research to Practice (pp 103-119). New York: Springer.; USE Lab; Student Explorer; https://hdl.handle.net/2027.42/107974; Learning Analytics: From Research to Practice

Availability: https://doi.org/10.1007/978-1-4614-3305-7_6
https://hdl.handle.net/2027.42/107974

Authors: Hardwick, Janis, Stout, Quentin F.

Contributors: CSE Department, University of Michigan, Ann Arbor, MI 48109, USA

Subject Terms: Computational and Graphical Statistics, Data Mining Statistics, Science

File Description: 114193 bytes; 3118 bytes; application/pdf; text/plain

Relation: Hardwick, Janis; Stout, Quentin F. (2009). "Algorithms for response adaptive sampling designs." Wiley Interdisciplinary Reviews: Computational Statistics 1(1): 118-122.; https://hdl.handle.net/2027.42/64301; Wiley Interdisciplinary Reviews: Computational Statistics

Availability: https://doi.org/10.1002/wics.25
https://hdl.handle.net/2027.42/64301

Authors: Zhao, Yan

Contributors: Liu, Henry, Peng, Huei, Yin, Yafeng, Masoud, Neda, Orosz, Gabor

Subject Terms: Traffic state estimation, Probe vehicle, Data mining, Transportation, Engineering

File Description: application/pdf

Relation: https://hdl.handle.net/2027.42/155289; orcid:0000-0002-5246-059X; Zhao, Yan; 0000-0002-5246-059X

Availability: https://hdl.handle.net/2027.42/155289

Authors: Zakarian, Armen, Sadoyan, Hovhannes, Mohanty, Pravansu

Contributors: Department of Mechanical Engineering, University of Michigan – Dearborn, Dearborn, MI, 48128, U.S.A., Department of Industrial and Manufacturing Systems Engineering, University of Michigan – Dearborn, USA, Dearborn, Ann Arbor

Subject Terms: Decision Rules, Rough Sets, Industrial and Production Engineering, Mechanical Engineering, Engineering, Computer-Aided Engineering (CAD, CAE) and Design, Production/Logistics, Data Mining, Manufacturing Process Control, Management, Information and Library Science, Industrial and Operations Engineering, Computer Science, Economics, Business, Social Sciences

File Description: 392390 bytes; 3115 bytes; application/pdf; text/plain

Relation: Sadoyan, Hovhannes; Zakarian, Armen; Mohanty, Pravansu; (2006). "Data mining algorithm for manufacturing process control." The International Journal of Advanced Manufacturing Technology 28 (3-4): 342-350.; https://hdl.handle.net/2027.42/45889; http://dx.doi.org/10.1007/s00170-004-2367-1; The International Journal of Advanced Manufacturing Technology

Availability: https://doi.org/10.1007/s00170-004-2367-1
https://hdl.handle.net/2027.42/45889

Authors: Hero, Alfred O. III, Fleury, Gilles

Contributors: Department of EECS, University of Michigan, Ann Arbor, MI, USA, Ecole Supérieure d'Electricité, Gif-sur-Yvette, France, Ann Arbor

Subject Terms: Engineering, Electronic and Computer Engineering, Signal, Image and Speech Processing, Circuits and Systems, Gene Filtering, Gene Screening, Multicriterion Scattergram, Data Mining, Posterior Pareto Fronts, Computer Science

File Description: 830581 bytes; 3115 bytes; application/pdf; text/plain

Relation: Hero, Alfred O.; Fleury, Gilles; (2004). "Pareto-Optimal Methods for Gene Ranking." The Journal of VLSI Signal Processing 38(3): 259-275.; https://hdl.handle.net/2027.42/41339; http://dx.doi.org/10.1023/B:VLSI.0000042491.03225.cf; The Journal of VLSI Signal Processing

Availability: https://doi.org/10.1023/B:VLSI.0000042491.03225.cf
https://hdl.handle.net/2027.42/41339

Authors: Iftikhar, Omar

Contributors: Dehzangi, Omid, Akingbehin, Kiumi, Shridhar, Malayappan

Subject Terms: Scoliosis, Brace treatment, Data mining, Activity-monitoring, Context-aware, Wearables, Compliance-monitoring, Computer science

File Description: application/pdf

Relation: https://hdl.handle.net/2027.42/143517; orcid:0000-0003-0493-601X; Iftikhar, Omar; 0000-0003-0493-601X

Availability: https://hdl.handle.net/2027.42/143517

Authors: Dong, Youchao

Contributors: Ma, Di, Wang, Shengquan, Guo, Jinhua

Subject Terms: Android security, Android permission, Statistical analysis, Malware prediction, Data mining, Machine learning, Computer and Information Science

File Description: application/pdf

Relation: https://hdl.handle.net/2027.42/136197; orcid:0000-0002-0106-778X; Dong, Youchao; 0000-0002-0106-778X

Availability: https://hdl.handle.net/2027.42/136197

Authors: Binzagr, Faisal

Contributors: Medjahed, Brahim, Guo, Jinhua, Malik, Hafiz

Subject Terms: Service Computing, Privacy, Service Interactions, K-anonymity, Data mining, Computer and Information Science

File Description: application/pdf

Relation: https://hdl.handle.net/2027.42/138104; orcid:0000-0001-9560-092X; BINZAGR, FAISAL; 0000-0001-9560-092X

Availability: https://hdl.handle.net/2027.42/138104

Authors: Melville, Alexander

Contributors: Dehzangi, Omid, Malik, Hafiz, Maxim, Bruce, Medjahed, Brahim, Ma, Di

Subject Terms: Data Science, Signal Processing, Wearables, Data Mining, Biomedical, EEG, Computer Science

File Description: application/pdf

Relation: https://hdl.handle.net/2027.42/136624; 8143640; orcid:0000-0002-5000-3352; Melville, Alexander; 0000-0002-5000-3352

Availability: https://hdl.handle.net/2027.42/136624

Authors: Wu, Harris

Contributors: Gordon, Michael D.

Subject Terms: Contextual Knowledge, Data Mining, Information Retrieval, Information Security, Managing, Online Document Repository, Structural Knowledge

File Description: 156 p.; application/pdf

Relation: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:3192819; https://hdl.handle.net/2027.42/125544

Availability: https://hdl.handle.net/2027.42/125544
http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:3192819