Survey Methodology

• Schonlau M, Weiß J, Marquardt J. Multi-label classification of open-ended questions with BERT. BIGSURV23, Quito, Ecuador, October 26-28, 2023. (link to proceedings), (arXiv preprint).
• Meitinger K., van der Sluis S, Schonlau M. Keep the noise down: On the performance of automatic speech recognition of voice recordings in web surveys. Survey Practice. Feb 2024. (link to journal)
• Gweon H. Schonlau M. Automated classification for open-ended questions with BERT. Journal of Survey Statistics and Methodology. (published online first 2023). (arXiv preprint) (journal link)
• He Z, Schonlau M. A model-assisted approach for finding coding errors in the manual coding of open-ended questions. Journal of Survey Statistics and Methodology, 10 (2) April 2022, pp 365-376. (free access link as allowed by journal)
• He Z, Schonlau M. Coding text answers to open-ended questions: human coders and statistical learning algorithms make similar mistakes. Methods, Data, Analyses. 15(1), 2021, pp. 103-120. (link to journal)
• Gweon H., Schonlau M, Wenemark M. Semi-automated classification for multi-label open-ended questions. Survey Methodology. Dec 2020, 46, 2, 265-282. (preprint)
• Schierholz M, Schonlau M. Machine Learning for Occupation Coding - A Comparison Study. Journal of Survey Statistics and Methodology. November 2021, 9(5), pp 1013-1034. (link to journal)
• He Z, Schonlau M. Automatic Coding of Open-ended Questions Into Multiple Classes: Whether and How to Use Double Coded data. Survey Research Methods. Aug 2020. 14(3), 267-287. (link to journal)
• Schonlau M, Gweon H, Wenemark, M. Automatic classification of open-ended questions: check-all-that-apply questions. Social Science Computer Review, 2021, 39(4), pp 562-572. (link to journal) (preprint)
• He Z, Schonlau M. Automatic Coding of Text Answers to Open-ended Questions: Should you Double Code the Training Data? Social Science Computer Review. Dec 2020. 38(6), pp. 754-765. (link to journal) (preprint)
• Schonlau M, Couper M. Options for Conducting Web surveys. Statistical Science, May 2017, 32(2), 279-292. (pdf)
• Gweon H, Schonlau M, Kaczmirek L, Blohm M, Steiner S. Three Methods for Occupation Coding Based on Statistical Learning. Journal of Official Statistics 2017, 33 (1), 101-122. (link to journal) (Link to R code on Github)
• McLauchlan C, Schonlau M. Are Final Comments in Web Survey Panels Associated with Next-Wave Attrition? Survey Research Methods, Dec 2016, 10(3), 211-224. (link to journal)
• Schonlau M, Couper M. Semi-automated categorization of open-ended questions. Survey Research Methods. August 2016, 10(2), 143-152. (link to journal)
• Schonlau M. What do web survey panel respondents answer when asked "Do you have any other comment?". Survey Methods: Insights from the Field. 2015. (link to journal)
• Schonlau M., Toepoel V. Straightlining in Web survey panels over time. Survey Research Methods, August 2015, 9(2), 125-137. (link to journal)
• Schonlau M, Weidmer B, Kapteyn, A. Recruiting an Internet Panel Using Respondent Driven Sampling. Journal of Official Statistics, June 2014; 30 (2): 277-289. (link to journal)
• Schonlau M, Watson N, Kroh, M. Cross-sectional weights in household survey panels. Statistics Surveys, 2013;7:37-57. DOI: 10.1214/13-SS104. (link to journal)
• Schonlau M, Liebau E. Respondent driven sampling, The Stata Journal, 2012; 12(1): 72-93 (pdf)
• Schonlau M, Watson N, Kroh M. Household survey panels: how much do following rules affect sample size? Survey Research Methods. 2011; 5(2):53-61. (link to journal)
• Schonlau M, Reuter M, Schupp J, Montag C, Weber B, Dohmen T, Siegel, NA, Sunde U, Wagner GG., Falk A. Collecting Genetic Samples in Population Wide (Panel) Surveys: Feasibility, Nonresponse and Selectivity. Survey Research Methods, 2010; 4(2): 121-126. (link to journal)
• Harris K M, Schonlau M, Lurie N. Surveying a Nationally Representative Internet-Based Panel to Obtain Timely Estimates of Influenza Vaccination Rates of Adults. Vaccine. 2009; 27:815-818.
• Schonlau M, Van Soest A, Kapteyn A, Couper M. Selection bias in Web surveys and the use of propensity scores. Sociological Methods and Research, 2009; 37(3) 291-318. (pdf)
• Schonlau M, Van Soest A, Kapteyn A, Are `Webographic' or attitudinal questions useful for adjusting estimates from Web surveys using propensity scoring? Survey Research Methods, 2007; 1(3): 155-163. (pdf)
• Couper M, Kapteyn A, Schonlau M, Winter J. Noncoverage and Nonresponse in an Internet Survey. Social Science Research. 2007; 36:131-148.
• Schonlau M. Will web surveys ever become part of mainstream research? Journal of Medical Internet Research. 2004; 6(3):e31. (pdf)
• Schonlau M, Zapert K, Payne Simon L, Sanstad K, Marcus S, Adams J, Spranca M, Kan H-J, Turner R, Berry S. A comparison between a propensity weighted web survey and an identical RDD survey. Social Science Computer Review. 2004; 22(1): 128-138. (pdf)
• Adams JL, Schonlau M, Escarce J, Kilgore M, Schoenbaum M, Goldman DP. Sampling Patients Within and Across Health Care Providers: Multi-Stage Non-nested Samples in Health Services Research. Health Services and Outcomes Research Methodology. 2003; 4(3): 151-167. (pdf)
• Schonlau M, Asch BJ, Du C. "Web surveys as part of a mixed mode strategy for populations that cannot be contacted by e-mail." Social Science Computer Review. 2003; 21(2):218-222. (pdf)
• Schonlau M, Fricker R, Elliott, M. "Conducting Research Surveys via E-Mail and the Web", RAND, Santa Monica, CA. 2002.
  This book is available on Amazon.com and at the RAND web site for this book.
  Book Reviews and Recommendations
• Fricker R, Schonlau M. "Advantages and disadvantages of Internet research surveys: Evidence from the literature." Field Methods. 2002;14(4):347-367. (pdf)

Statistical Learning

• Yang T, Sucholutsky I, Jen KY, Schonlau M. exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies. PeerJ Computer Science. Feb 2024. (link to journal)
• Sucholutsky I, Schonlau M. `Less than one'-shot learning: Learning N classes from M < N samples. Proceedings of the thirty-fifth Conference on Artificial Intelligence (AAAI'21). Feb 2021. pp 9739-9746. (link to arXiv)
• Sucholutsky I, Schonlau M. SecDD: Efficient and Secure Method for Remotely Training Neural Networks. Proceedings of the thirty-fifth Conference on Artificial Intelligence (AAAI'21). Extended abstract. Feb 2021, pp 15897-15898. (link to arXiv)
• Sucholutsky I, Schonlau M. Soft-Label Dataset Distillation and Text Dataset Distillation. The International Joint Conference on Neural Networks (IJCNN21). 18-22 July 2021, pp 1-8. (link to arXiv)
• Sucholutsky I, Kim N-H, Browne RP, and Schonlau M. One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes. The International Joint Conference on Neural Networks (IJCNN21), 18-22 July 2021, pp1-8. (link to arXiv)
• Sucholutsky I, Schonlau M. Optimal 1-NN prototypes for pathological geometries. PeerJ Computer Science, April 2021, 7, e464, 1-17, (link to journal)
• Schonlau M, Zou, R. Y. The Random Decision Forest Algorithm for Statistical Learning. The Stata Journal. Mar 2020. 20(1), 3-29. (pdf)
• Gweon H, Schonlau M, Steiner S. The conditional nearest neighbor algorithm for classification. PeerJ Computer Science. May 13, 2019. 5, e194,1-21. (link to journal)
• Sucholutsky I, Narayan A, Schonlau M, Fischmeister S. Pay attention and you won't lose it: a deep learning approach to sequence imputation. PeerJ Computer Science. August 12, 2019. 5:e210. (link to journal)
• Sucholutsky I, Narayan A, Schonlau M, and Fischmeister S. Deep Learning for System Trace Restoration, Proceedings of The International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 14-19, 2019, p1-8, (link to journal)
• Gweon H, Schonlau M, Steiner S. Nearest Labelset Using Double Distances for Multi-label Classification. PeerJ Computer Science, December 9, 2019. 5:e242,1-20. (link to journal)
• Sucholutsky I, Schonlau M. ConvART: Improving Adaptive Resonance Theory for Unsupervised Image Clustering. Journal of Computational Vision and Imaging Systems. 2018, 4(1),1-2. (link to journal)
• Gweon H, Schonlau M, Steiner S. The conditional nearest neighbor algorithm for classification. PeerJ Computer Science. May 13, 2019. 5, e194,1-21. (link to journal)
• Schonlau M, Guenther N, Sucholutsky I. Text mining using n-gram variables. The Stata Journal. Dec 2017, 17(4), 866-881. (pdf)
• Guenther N, Schonlau M, Support vector machines. The Stata Journal. Dec 2016, 16(4), 917-937. (pdf)
• Schonlau M. Boosted Regression (Boosting): An introductory tutorial and a Stata plugin. The Stata Journal, 2005; 5(3):330-354. (pdf)
• Raghavan N, Bell R, Schonlau M, Pregibon D, Karr A. (2000). "Defection detection: Using online activity profiles to predict ISP customer vulnerability" in Proceedings: The Sixth International Conference of Knowledge Discovery and Data Mining, August , Boston, 506-515. (pdf)

Visualization / Graphics

• Schonlau M. Hammock plots: visualizing categorical and numerical variables. Journal of Computational and Graphical Statistics (to appear) (preprint) (journal link)
• Schonlau M, Peters E. Comprehension of Graphs and Tables Depends on the Task: Empirical Evidence from two web-based studies. Statistics, Politics and Policy. 2012; 3 (2):Article 5. (pdf)
• Schonlau M. Visualizing Categorical Data Arising in the Health Sciences Using Hammock Plots. In Proceedings of the Section on Statistical Graphics, American Statistical Association; 2003. (pdf)
• Schonlau M. Visualizing Hierarchical and Non-Hierarchical Cluster Analyses with Clustergrams. Computational Statistics: 2004; 19(1): 95-111. (pdf)
• Schonlau M. The clustergram: a graph for visualizing hierarchical and non-hierarchical cluster analyses. The Stata Journal, 2002; 2 (4):391-402. (pdf)

Health Literacy

• Schonlau M, Martin LT, Haas A, Derose K, Rudd R. Patients’ Literacy Skills: More than just reading ability? Journal of Health Communication, 2011; 16(10): 1046-1054.
• Martin LT, Schonlau M, Haas A, Derose K, Rudd R, Loucks EB, Rosenfeld L, Buka SL. Health Literacy Skills and Risk of Coronary Heart Disease. Journal of General Internal Medicine. 2011; 26(1): 45-50.
• Martin L, Haas A, Schonlau M., Pitkin Derose K, Rosenfeld L, Rudd R, Buka S. Which Literacy Skills are Associated with Smoking? Journal of Epidemiology and Community Health. 2011. DOI:10.1136/jech.2011.136341.
• Martin L, Schonlau, M, Haas A, Rosenfeld L, Pitkin Derose K, Buka, S, Rudd R. Patient activation and advocacy: Which Literacy Skills Matter Most? Journal of Health Communication, 2011; 16(Suppl 3):177-190.

Criminology

• Schonlau M. Charging Decisions in Death-Eligible Federal Cases (1995-2000): Arbitrariness, Capriciousness, and Regional Variation in: Klein , Berk and Hickman (editors) Race and the Decision to Seek the Death Penalty in Federal Cases. RAND Corporation, 2006, TR-389-NIJ.
RAND website (see chapter 4 in pdf)

Health Sciences

• Martin, L., Burns, R. Schonlau M. Mental Disorders Among Gifted and Nongifted Youth: A Selected Review of the Epidemiologic Literature. Gifted Child Quarterly. 2010; 54 (1), 31-41. Journal page.
• Miller DC, Schonlau M, Litwin MS, Lai J, Saigal CS, and the Urologic Diseases in America Project. Renal and Cardiovascular Morbidity After Partial or Radical Nephrectomy, Cancer, Feb 1 2008; 112(3): 511-521.
• Schonlau M, Mangione-Smith R, Rosen M, Chan K, Louis T, Wu S, Keeler E. An evaluation of an adult asthma BTS collaborative and the effect of patient self-management, Annals of Family Medicine. Ann Fam Med 2005 3: 200-208. (pdf on www.annfammed.org)
• Chan K, Keeler E, Schonlau M, Rosen M, Mangione-Smith R. How do Ethnicity and Primary Language Spoken at Home Affect Management Practices and Outcomes in Children with Asthma? Archives of Pediatric and Adolescent Medicine. 2005; 159 (Mar): 283-289.
• Mangione-Smith R, Schonlau M, Rosen M, Chan K, Louis T, Keeler E. Measuring the Effectiveness of a Collaborative for Quality Improvement in Pediatric Asthma Care: Does Implementing the Chronic Care Model Improve Processes and Outcomes of Care? Ambulatory Pediatrics. 2005; 5 (2): 75-82.
• Goldman D, Berry S, McCabe M, Kilgore M, Potosky A, Schoenbaum M, Schonlau M, Weeks J, Kaplan R, Escarce J. Incremental Treatment Costs in NCI- Sponsored Clinical Trials, JAMA. 2003; 289(22): 2970-2977.
• Lewis JH, Schonlau M, Muñoz J, Asch SM, Rosen MR, Yang H, Escarce JJ. Pharmacy Compliance With California's Prescription Drug Discount Program for Medicare Beneficiaries. The New England Journal of Medicine. 2002;346(11):830-835. (pdf)

Computer Experiments / Global Optimization

• Schonlau M., Welch WJ. Screening the Input Variables to a Computer Code Via Analysis of Variance and Visualization. In Screening: Methods for Experimentation in Industry, Drug Discovery and Genetics. Eds: A. M. Dean and S. M. Lewis, Springer Verlag, New York; 2006:308-327.
• Jones, D., Schonlau, M., Welch, W., (1998) ``Efficient Global Optimization of Expensive Black-Box Functions''. Journal of Global Optimization} Vol. 13, 455-492. (pdf)
• Schonlau, M., Welch, W., Jones, D. (1998), "Global Versus Local Search in Constrained Optimization of Computer Models," in New Developments and Applications in Experimental Design, N. Flournoy, W.F. Rosenberger, and W.K. Wong (editors), Institute of Mathematical Statistics, Hayward, California, Vol. 34, 11-25. (pdf)
• Mrawira, D., Welch, W., Schonlau, M., Haas, R. (1999) "Sensitivity Analysis of Computer Models: The World Bank HDM-III Model" Journal of Transportation Engineering, 421-428. (pdf) (Figure 2 pdf)
• Schonlau, M., Welch, W., Jones, D. (1997) A Data-Analytic Approach to Bayesian Global Optimization . American Statistical Association Proceedings, Section of Physical Engineering Sciences, 186-191. (pdf)
• Matthias Schonlau (1997). "Computer Experiments and Global Optimization" Ph.D. Dissertation, University of Waterloo. (1.1 MB, approx 130 pages, (pdf)
• Schonlau, M., Welch, W. (1996). Global Optimization with Nonparametric Function Fitting. Proceedings of the Section on Physical and Engineering Sciences}, American Statistical Association, 183-186. (pdf)
• Welch, W.J, Buck, R.J., Sacks, J., Wynn, H.P., Mitchell, T.J., Morris, M.D., Schonlau, M. (1996), Rejoinder to "Comments on Computer Experiments" by James M. Lucas, Technometrics, 38,199-203. (pdf)
• Schonlau, M., Hamada, M., Welch, W. (1995). Nonparametric Function-Fitting To Suggest Nonlinear Parametric Models. American Statistical Association Proceedings, Section of Physical Engineering Sciences, 262-267 (pdf)

Computer intrusion detection/ Counter Terrorism

• Stoto M , Schonlau M., Mariano L. Syndromic Surveillance : Is it Worth the Effort? Chance. 2004: 17, 1, 19-24.
• Schonlau, M., DuMouchel, W., Ju, W., Karr, A., Theus, M., Vardi, Y. (2001), "Computer Intrusion: Detecting Masquerades," Statistical Science, 2001;16(1):58-74. (pdf)
• Schonlau, M., Theus, M. (2000), "Detecting Masquerades in Intrusion Detection Based on Unpopular Commands," Information Processing Letters, 76, 33-38. (pdf)
• Theus, M., Schonlau, M., 1998, ``Intrusion Detection Based on Structural Zeroes''.  Statistical Computing & Graphics Newsletter. Vol. 9, No 1, 12 - 17. (pdf)
• DuMouchel, W., Schonlau, M., ``A fast computer intrusion detection algorithm based on hypothesis testing of command transition probabilities''.  The Fourth International Conference of Knowledge Discovery and Data Mining, August 27-31, New York, pp. 189-193. (pdf)
• DuMouchel, W., Schonlau, M. (1999), "A Comparison of Test Statistics for Computer Intrusion Detection Based on Principal Components Regression of Transition Probabilities," Proceedings of the 30th Symposium on the Interface: Computing Science and Statistics, 30, 404-413. (pdf)