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Nicholas Jon Horton

Personal Details

First Name:Nicholas
Middle Name:Jon
Last Name:Horton
Suffix:
RePEc Short-ID:pho144
http://www.math.smith.edu/~nhorton
Smith College Clark Science Center 44 College Lane Northampton, MA 01063-0001

Affiliation

(95%) Department of Mathematics and Statistics

https://www.amherst.edu/academiclife/departments/mathematics
Amherst, MA

(5%) Smith College, Department of Mathematics and Statistics (Smith College, Department of Mathematics)

http://www.smith.edu
United States of America, Northampton, MA

Research output

as
Jump to: Working papers Articles

Working papers

  1. Nicholas Jon Horton, 2007. "Agony and ecstasy: teaching a computationally intensive introductory statistics course using Stata," North American Stata Users' Group Meetings 2007 10, Stata Users Group.
  2. Nicholas Horton & Garrett Fitzmaurice, 2005. "Analysis of multiple source/multiple informant data in Stata," North American Stata Users' Group Meetings 2005 1, Stata Users Group.
  3. Nicholas Horton, 2001. "Fitting Generalized Estimating Equation (GEE) Regression Models in Stata," North American Stata Users' Group Meetings 2001 1.1, Stata Users Group.

Articles

  1. Xiaofei Wang & Nicholas G. Reich & Nicholas J. Horton, 2019. "Enriching Students’ Conceptual Understanding of Confidence Intervals: An Interactive Trivia-Based Classroom Activity," The American Statistician, Taylor & Francis Journals, vol. 73(1), pages 50-55, January.
  2. Amelia McNamara & Nicholas J. Horton, 2018. "Wrangling Categorical Data in R," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 97-104, January.
  3. Nicholas Jon Horton, 2016. "Discussion: Making Progress in a Crowded Market," International Statistical Review, International Statistical Institute, vol. 84(2), pages 179-181, August.
  4. Nicholas J. Horton, 2015. "Challenges and Opportunities for Statistics and Statistical Education: Looking Back, Looking Forward," The American Statistician, Taylor & Francis Journals, vol. 69(2), pages 138-145, May.
  5. J. Hardin & R. Hoerl & Nicholas J. Horton & D. Nolan & B. Baumer & O. Hall-Holt & P. Murrell & R. Peng & P. Roback & D. Temple Lang & M. D. Ward, 2015. "Data Science in Statistics Curricula: Preparing Students to “Think with Data”," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 343-353, November.
  6. Nicholas J. Horton & Johanna S. Hardin, 2015. "Teaching the Next Generation of Statistics Students to “Think With Data”: Special Issue on Statistics and the Undergraduate Curriculum," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 259-265, November.
  7. Mahnaz Mahdavi & Nicholas J. Horton, 2014. "Financial Knowledge among Educated Women: Room for Improvement," Journal of Consumer Affairs, Wiley Blackwell, vol. 48(2), pages 403-417, June.
  8. Kathryn M. Aloisio & Sonja A. Swanson & Nadia Micali & Alison Field & Nicholas J. Horton, 2014. "Analysis of partially observed clustered data using generalized estimating equations and multiple imputation," Stata Journal, StataCorp LP, vol. 14(4), pages 863-883, December.
  9. Nicholas J. Horton, 2013. "I Hear, I Forget. I Do, I Understand: A Modified Moore-Method Mathematical Statistics Course," The American Statistician, Taylor & Francis Journals, vol. 67(4), pages 219-228, November.
  10. C. J. Wild & M. Pfannkuch & M. Regan & N. J. Horton, 2011. "Towards more accessible conceptions of statistical inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 247-295, April.
  11. Nicholas J. Horton, 2011. "Stata tip 95: Estimation of error covariances in a linear model," Stata Journal, StataCorp LP, vol. 11(1), pages 145-148, March.
  12. Maria Paola Caria & Rino Bellocco & Maria Rosaria Galanti & Nicholas J. Horton, 2011. "The impact of different sources of body mass index assessment on smoking onset: An application of multiple-source information models," Stata Journal, StataCorp LP, vol. 11(3), pages 386-402, September.
  13. Nicholas J. Horton, 2008. "Review of Multilevel and Longitudinal Modeling Using Stata, Second Edition, by Sophia Rabe-Hesketh and Anders Skrondal," Stata Journal, StataCorp LP, vol. 8(4), pages 579-582, December.
  14. Horton, Nicholas J. & Kleinman, Ken P., 2007. "Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models," The American Statistician, American Statistical Association, vol. 61, pages 79-90, February.
  15. Cozier, Y.C. & Palmer, J.R. & Horton, N.J. & Fredman, L. & Wise, L.A. & Rosenberg, L., 2007. "Relation between neighborhood median housing value and hypertension risk among black women in the United States," American Journal of Public Health, American Public Health Association, vol. 97(4), pages 718-724.
  16. Horton, Nicholas J., 2006. "Multilevel and Longitudinal Modeling Using Stata. Sophia Rabe-Hesketh and Anders Skrondal," The American Statistician, American Statistical Association, vol. 60, pages 293-294, August.
  17. Horton, Nicholas J. & Brown, Elizabeth R. & Qian, Linjuan, 2004. "Use of R as a Toolbox for Mathematical Statistics Exploration," The American Statistician, American Statistical Association, vol. 58, pages 343-357, November.
  18. Palmer, J.R. & Rosenberg, L. & Wise, L.A. & Horton, N.J. & Adams-Campbell, L.L., 2003. "Onset of natural menopause in African American women," American Journal of Public Health, American Public Health Association, vol. 93(2), pages 299-306.
  19. Horton N.J. & Lipsitz S.R. & Parzen M., 2003. "A Potential for Bias When Rounding in Multiple Imputation," The American Statistician, American Statistical Association, vol. 57, pages 229-232, November.
  20. Nicholas J. Horton & Garrett M. Fitzmaurice, 2002. "Maximum likelihood estimation of bivariate logistic models for incomplete responses with indicators of ignorable and non‐ignorable missingness," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(3), pages 281-295, July.
  21. Nicholas J. Horton & Nan M. Laird, 2001. "Maximum Likelihood Analysis of Logistic Regression Models with Incomplete Covariate Data and Auxiliary Information," Biometrics, The International Biometric Society, vol. 57(1), pages 34-42, March.
  22. Joseph G. Ibrahim & Stuart R. Lipsitz & Nick Horton, 2001. "Using auxiliary data for parameter estimation with non‐ignorably missing outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 361-373.
  23. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Nicholas Horton & Garrett Fitzmaurice, 2005. "Analysis of multiple source/multiple informant data in Stata," North American Stata Users' Group Meetings 2005 1, Stata Users Group.

    Cited by:

    1. Maria Paola Caria & Rino Bellocco & Maria Rosaria Galanti & Nicholas J. Horton, 2011. "The impact of different sources of body mass index assessment on smoking onset: An application of multiple-source information models," Stata Journal, StataCorp LP, vol. 11(3), pages 386-402, September.
    2. Xu Xu & Peter Z. G. Qian & Qing Liu, 2016. "Samurai Sudoku-Based Space-Filling Designs for Data Pooling," The American Statistician, Taylor & Francis Journals, vol. 70(1), pages 1-8, February.

  2. Nicholas Horton, 2001. "Fitting Generalized Estimating Equation (GEE) Regression Models in Stata," North American Stata Users' Group Meetings 2001 1.1, Stata Users Group.

    Cited by:

    1. Debasish Kumar Das & Champa Bati Dutta, 2013. "Global Financial Crisis And Foreign Development Assistance Shocks In Least Developing Countries," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 38(2), pages 1-41, June.

Articles

  1. Nicholas Jon Horton, 2016. "Discussion: Making Progress in a Crowded Market," International Statistical Review, International Statistical Institute, vol. 84(2), pages 179-181, August.

    Cited by:

    1. Iddo Gal & Irena Ograjenšek, 2016. "Rejoinder: More on Enhancing Statistics Education with Qualitative Ideas," International Statistical Review, International Statistical Institute, vol. 84(2), pages 202-209, August.

  2. Nicholas J. Horton, 2015. "Challenges and Opportunities for Statistics and Statistical Education: Looking Back, Looking Forward," The American Statistician, Taylor & Francis Journals, vol. 69(2), pages 138-145, May.

    Cited by:

    1. Cimpoeru, Smaranda & Roman, Monica, 2018. "Statistical Literacy and Attitudes Towards Statistics of Romanian Undergraduate Students," MPRA Paper 90452, University Library of Munich, Germany, revised 31 Aug 2018.
    2. Constance H. McLaren & Bruce J. McLaren, 2018. "SCOTS: The Searchable Collection of Time Series," INFORMS Transactions on Education, INFORMS, vol. 19(1), pages 12-22, September.
    3. Lisa Dierker & Jane Robertson Evia & Karen Singer-Freeman & Kristin Woods & Janet Zupkus & Alan Arnholt & Elizabeth G Moliski & Natalie Delia Deckard & Kristel Gallagher & Jennifer Rose, 2018. "Project-Based Learning in Introductory Statistics: Comparing Course Experiences and Predicting Positive Outcomes for Students from Diverse Educational Settings," International Journal of Educational Technology and Learning, Scientific Publishing Institute, vol. 3(2), pages 52-64.
    4. Sitsofe Tsagbey & Miguel de Carvalho & Garritt L. Page, 2017. "All Data are Wrong, but Some are Useful? Advocating the Need for Data Auditing," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 231-235, July.

  3. J. Hardin & R. Hoerl & Nicholas J. Horton & D. Nolan & B. Baumer & O. Hall-Holt & P. Murrell & R. Peng & P. Roback & D. Temple Lang & M. D. Ward, 2015. "Data Science in Statistics Curricula: Preparing Students to “Think with Data”," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 343-353, November.

    Cited by:

    1. Roger W. Hoerl & Ronald D. Snee, 2017. "Statistical Engineering: An Idea Whose Time Has Come?," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 209-219, July.
    2. David Gil & Magnus Johnsson & Higinio Mora & Julian Szymański, 2019. "Review of the Complexity of Managing Big Data of the Internet of Things," Complexity, Hindawi, vol. 2019, pages 1-12, February.
    3. Simons, Andrew M., 2020. "Making Business Statistics Come Alive: Incorporating Field Trial Data from a Cookstove Study into the Classroom," Applied Economics Teaching Resources (AETR), Agricultural and Applied Economics Association, vol. 2(3), July.

  4. Nicholas J. Horton & Johanna S. Hardin, 2015. "Teaching the Next Generation of Statistics Students to “Think With Data”: Special Issue on Statistics and the Undergraduate Curriculum," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 259-265, November.

    Cited by:

    1. Roger W. Hoerl & Ronald D. Snee, 2017. "Statistical Engineering: An Idea Whose Time Has Come?," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 209-219, July.
    2. Alexander B. Sibley & Zhiguo Li & Yu Jiang & Yi-Ju Li & Cliburn Chan & Andrew Allen & Kouros Owzar, 2018. "Facilitating the Calculation of the Efficient Score Using Symbolic Computing," The American Statistician, Taylor & Francis Journals, vol. 72(2), pages 199-205, April.
    3. Nicholas Jon Horton, 2016. "Discussion: Making Progress in a Crowded Market," International Statistical Review, International Statistical Institute, vol. 84(2), pages 179-181, August.
    4. Joel B. Greenhouse & Howard J. Seltman, 2018. "On Teaching Statistical Practice: From Novice to Expert," The American Statistician, Taylor & Francis Journals, vol. 72(2), pages 147-154, April.
    5. Constance H. McLaren & Bruce J. McLaren, 2018. "SCOTS: The Searchable Collection of Time Series," INFORMS Transactions on Education, INFORMS, vol. 19(1), pages 12-22, September.
    6. Amy L. Phelps & Kathryn A. Szabat, 2017. "The Current Landscape of Teaching Analytics to Business Students at Institutions of Higher Education: Who is Teaching What?," The American Statistician, Taylor & Francis Journals, vol. 71(2), pages 155-161, April.

  5. Mahnaz Mahdavi & Nicholas J. Horton, 2014. "Financial Knowledge among Educated Women: Room for Improvement," Journal of Consumer Affairs, Wiley Blackwell, vol. 48(2), pages 403-417, June.

    Cited by:

    1. Boggio, Cecilia & Coda Moscarola, Flavia & Gallice, Andrea, 2020. "What is good for the goose is good for the gander?," Economics of Education Review, Elsevier, vol. 75(C).
    2. Annamaria Lusardi & Olivia S. Mitchell, 2014. "The Economic Importance of Financial Literacy: Theory and Evidence," Journal of Economic Literature, American Economic Association, vol. 52(1), pages 5-44, March.
    3. Antonia Grohmann & Annekathrin Schoofs, 2018. "Financial Literacy and Intra-Household Decision Making: Evidence from Rwanda," Discussion Papers of DIW Berlin 1720, DIW Berlin, German Institute for Economic Research.
    4. Bucher-Koenen, Tabea & Lusardi, Annamaria & Alessie, Rob J. M. & Van Rooij, Maarten C. J., 2014. "How Financially Literate are Women? An Overview and New Insights," MEA discussion paper series 201419, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    5. Alfonso Arellano & Noelia Camara & David Tuesta, 2014. "El efecto de la autoconfianza en el conocimiento financiero," Working Papers 1427, BBVA Bank, Economic Research Department.
    6. Alfonso Arellano & Noelia Camara & David Tuesta, 2014. "The effect of self-confidence on financial literacy," Working Papers 1428, BBVA Bank, Economic Research Department.
    7. Davoli, Maddalena & Rodríguez-Planas, Núria, 2020. "Culture and Adult Financial Literacy: Evidence from the United States," IZA Discussion Papers 13349, Institute of Labor Economics (IZA).
    8. Alfonso Arellano & Noelia Camara & David Tuesta, 2015. "Explaining the Gender Gap in Financial Literacy: the Role of Non-Cognitive Skills," Working Papers 15/32, BBVA Bank, Economic Research Department.
    9. Antonia Grohmann & Olaf Hübler & Roy Kouwenberg & Lukas Menkhoff, 2016. "Financial Literacy: Thai Middle Class Women Do Not Lag behind," Discussion Papers of DIW Berlin 1615, DIW Berlin, German Institute for Economic Research.

  6. Kathryn M. Aloisio & Sonja A. Swanson & Nadia Micali & Alison Field & Nicholas J. Horton, 2014. "Analysis of partially observed clustered data using generalized estimating equations and multiple imputation," Stata Journal, StataCorp LP, vol. 14(4), pages 863-883, December.

    Cited by:

    1. Liu, Li & Xiang, Liming, 2019. "Missing covariate data in generalized linear mixed models with distribution-free random effects," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 1-16.

  7. Nicholas J. Horton, 2013. "I Hear, I Forget. I Do, I Understand: A Modified Moore-Method Mathematical Statistics Course," The American Statistician, Taylor & Francis Journals, vol. 67(4), pages 219-228, November.

    Cited by:

    1. Nicholas J. Horton, 2015. "Challenges and Opportunities for Statistics and Statistical Education: Looking Back, Looking Forward," The American Statistician, Taylor & Francis Journals, vol. 69(2), pages 138-145, May.

  8. C. J. Wild & M. Pfannkuch & M. Regan & N. J. Horton, 2011. "Towards more accessible conceptions of statistical inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 247-295, April.

    Cited by:

    1. Chris J. Wild & Maxine Pfannkuch & Matt Regan & Ross Parsonage, 2017. "Accessible Conceptions of Statistical Inference: Pulling Ourselves Up by the Bootstraps," International Statistical Review, International Statistical Institute, vol. 85(1), pages 84-107, April.
    2. Xie, Yihui, 2013. "animation: An R Package for Creating Animations and Demonstrating Statistical Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i01).
    3. Jim Ridgway, 2016. "Implications of the Data Revolution for Statistics Education," International Statistical Review, International Statistical Institute, vol. 84(3), pages 528-549, December.

  9. Nicholas J. Horton, 2008. "Review of Multilevel and Longitudinal Modeling Using Stata, Second Edition, by Sophia Rabe-Hesketh and Anders Skrondal," Stata Journal, StataCorp LP, vol. 8(4), pages 579-582, December.

    Cited by:

    1. Nicholas J. Horton, 2011. "Stata tip 95: Estimation of error covariances in a linear model," Stata Journal, StataCorp LP, vol. 11(1), pages 145-148, March.

  10. Horton, Nicholas J. & Kleinman, Ken P., 2007. "Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models," The American Statistician, American Statistical Association, vol. 61, pages 79-90, February.

    Cited by:

    1. R Florez-Lopez, 2010. "Effects of missing data in credit risk scoring. A comparative analysis of methods to achieve robustness in the absence of sufficient data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 486-501, March.
    2. Hapfelmeier, A. & Hothorn, T. & Ulm, K., 2012. "Recursive partitioning on incomplete data using surrogate decisions and multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1552-1565.
    3. Celeste Combrinck & Vanessa Scherman & David Maree & Sarah Howie, 2018. "Multiple Imputation for Dichotomous MNAR Items Using Recursive Structural Equation Modeling With Rasch Measures as Predictors," SAGE Open, , vol. 8(1), pages 21582440187, February.
    4. Simon Trimborn & Wolfgang Karl Härdle, 2015. "CRIX or evaluating Blockchain based currencies," SFB 649 Discussion Papers SFB649DP2015-048, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Trimborn, Simon & Härdle, Wolfgang Karl, 2018. "CRIX an Index for cryptocurrencies," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 107-122.
    6. Yi-Sheng Chao & Hsing-Chien Wu & Chao-Jung Wu & Wei-Chih Chen, 2018. "Index or illusion: The case of frailty indices in the Health and Retirement Study," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-19, July.
    7. Zhong, Hua & Hu, Wuyang, 2015. "Farmers’ Willingness to Engage in Best Management Practices: an Application of Multiple Imputation," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196962, Southern Agricultural Economics Association.
    8. Lena Walther & Lukas M. Fuchs & Jürgen Schupp & Christian von Scheve, 2019. "Living Conditions and the Mental Health and Well-being of Refugees: Evidence from a Representative German Panel Study," SOEPpapers on Multidisciplinary Panel Data Research 1029, DIW Berlin, The German Socio-Economic Panel (SOEP).
    9. Dardanoni, Valentino & Modica, Salvatore & Peracchi, Franco, 2011. "Regression with imputed covariates: A generalized missing-indicator approach," Journal of Econometrics, Elsevier, vol. 162(2), pages 362-368, June.
    10. Siddique, Juned & Harel, Ofer, 2009. "MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i09).
    11. McDonough, Ian K. & Millimet, Daniel L., 2017. "Missing data, imputation, and endogeneity," Journal of Econometrics, Elsevier, vol. 199(2), pages 141-155.
    12. Louis Anthony (Tony) Cox, Jr & Douglas A. Popken, 2008. "Overcoming Confirmation Bias in Causal Attribution: A Case Study of Antibiotic Resistance Risks," Risk Analysis, John Wiley & Sons, vol. 28(5), pages 1155-1172, October.
    13. David (David Patrick) Madden, 2012. "The relationship between low birthweight and socioeconomic status in Ireland," Working Papers 201214, School of Economics, University College Dublin.
    14. Cain Polidano & Ha Vu, 2012. "Labour market impacts from disability onset," ANU Working Papers in Economics and Econometrics 2012-583, Australian National University, College of Business and Economics, School of Economics.
    15. Brady T. West & Patricia Berglund & Steven G. Heeringa, 2008. "A closer examination of subpopulation analysis of complex-sample survey data," Stata Journal, StataCorp LP, vol. 8(4), pages 520-531, December.
    16. Consentino, Fabrizio & Claeskens, Gerda, 2010. "Order selection tests with multiply imputed data," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2284-2295, October.
    17. Gedikoglu, Haluk & Parcell, Joseph L., 2013. "Implications of Survey Sampling Design for Missing Data Imputation," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149679, Agricultural and Applied Economics Association.
    18. Schomaker, Michael & Heumann, Christian, 2014. "Model selection and model averaging after multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 758-770.
    19. Rachel Griffith & Rodrigo Lluberas & Melanie Lührmann, 2016. "Gluttony And Sloth? Calories, Labor Market Activity And The Rise Of Obesity," Journal of the European Economic Association, European Economic Association, vol. 14(6), pages 1253-1286, December.
    20. Hapfelmeier, A. & Ulm, K., 2014. "Variable selection by Random Forests using data with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 129-139.
    21. Margaret L McNairy & Deanna Jannat-Khah & Jean W Pape & Adias Marcelin & Patrice Joseph & Jean Edward Mathon & Serena Koenig & Martin Wells & Daniel W Fitzgerald & Arthur Evans, 2018. "Predicting death and lost to follow-up among adults initiating antiretroviral therapy in resource-limited settings: Derivation and external validation of a risk score in Haiti," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-16, August.
    22. Valentino Dardanoni & Giuseppe De Luca & Salvatore Modica & Franco Peracchi, 2011. "A Generalized Missing-Indicator Approach to Regression with Imputed Covariates," EIEF Working Papers Series 1111, Einaudi Institute for Economics and Finance (EIEF), revised May 2011.
    23. Präg, Patrick, 2018. "Nonresponse to Items on Self-Reported Delinquency. A Review and Evaluation of Missing Data Techniques," SocArXiv y9sv7, Center for Open Science.
    24. James Honaker & Gary King, 2010. "What to Do about Missing Values in Time‐Series Cross‐Section Data," American Journal of Political Science, John Wiley & Sons, vol. 54(2), pages 561-581, April.
    25. Lê, Félice & Diez Roux, Ana & Morgenstern, Hal, 2013. "Effects of child and adolescent health on educational progress," Social Science & Medicine, Elsevier, vol. 76(C), pages 57-66.
    26. Jushan Bai & Serena Ng, 2019. "Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data," Papers 1910.06677, arXiv.org, revised May 2020.
    27. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
    28. Fulvio Castellacci & José Miguel Natera, 2011. "A new panel dataset for cross-country analyses of national systems, growth and development (CANA)," Working Papers del Instituto Complutense de Estudios Internacionales 1105, Universidad Complutense de Madrid, Instituto Complutense de Estudios Internacionales.
    29. Kristian Kleinke & Mark Stemmler & Jost Reinecke & Friedrich Lösel, 2011. "Efficient ways to impute incomplete panel data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 351-373, December.

  11. Cozier, Y.C. & Palmer, J.R. & Horton, N.J. & Fredman, L. & Wise, L.A. & Rosenberg, L., 2007. "Relation between neighborhood median housing value and hypertension risk among black women in the United States," American Journal of Public Health, American Public Health Association, vol. 97(4), pages 718-724.

    Cited by:

    1. Harrington, Daniel W. & Elliott, Susan J., 2009. "Weighing the importance of neighbourhood: A multilevel exploration of the determinants of overweight and obesity," Social Science & Medicine, Elsevier, vol. 68(4), pages 593-600, February.
    2. Arline T. Geronimus & John Bound & Annie Ro, 2014. "Residential Mobility Across Local Areas In The United States And The Geographic Distribution Of The Healthy Population," Working Papers 14-14, Center for Economic Studies, U.S. Census Bureau.
    3. Bécares, Laia & Nazroo, James & Jackson, James & Heuvelman, Hein, 2012. "Ethnic density effects on health and experienced racism among Caribbean people in the US and England: A cross-national comparison," Social Science & Medicine, Elsevier, vol. 75(12), pages 2107-2115.

  12. Horton, Nicholas J. & Brown, Elizabeth R. & Qian, Linjuan, 2004. "Use of R as a Toolbox for Mathematical Statistics Exploration," The American Statistician, American Statistical Association, vol. 58, pages 343-357, November.

    Cited by:

    1. Nicholas J. Horton, 2015. "Challenges and Opportunities for Statistics and Statistical Education: Looking Back, Looking Forward," The American Statistician, Taylor & Francis Journals, vol. 69(2), pages 138-145, May.

  13. Horton N.J. & Lipsitz S.R. & Parzen M., 2003. "A Potential for Bias When Rounding in Multiple Imputation," The American Statistician, American Statistical Association, vol. 57, pages 229-232, November.

    Cited by:

    1. Yeh Jason Jia-Hsing, 2009. "Missing (Completely?) At Random: Lessons from Insurance Studies," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 3(2), pages 1-13, April.
    2. R Florez-Lopez, 2010. "Effects of missing data in credit risk scoring. A comparative analysis of methods to achieve robustness in the absence of sufficient data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 486-501, March.
    3. Celeste Combrinck & Vanessa Scherman & David Maree & Sarah Howie, 2018. "Multiple Imputation for Dichotomous MNAR Items Using Recursive Structural Equation Modeling With Rasch Measures as Predictors," SAGE Open, , vol. 8(1), pages 21582440187, February.
    4. Kajal Lahiri & Zulkarnain Pulungan, 2006. "Health Inequality and Its Determinants in New York," Discussion Papers 06-03, University at Albany, SUNY, Department of Economics.
    5. Matthew Desmond & Tracey Shollenberger, 2015. "Forced Displacement From Rental Housing: Prevalence and Neighborhood Consequences," Demography, Springer;Population Association of America (PAA), vol. 52(5), pages 1751-1772, October.
    6. Carsten Kuchler & Martin Spiess, 2009. "The data quality concept of accuracy in the context of publicly shared data sets," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 3(1), pages 67-80, June.
    7. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    8. Xu, Wan & Khachatryan, Hayk, 2014. "Multiple Imputation in the Complex National Nursery Survey Data by Fully Conditional Specification," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170208, Agricultural and Applied Economics Association.
    9. Danielle X. Morales & Sara E. Grineski & Timothy W. Collins, 2017. "Faculty Motivation to Mentor Students Through Undergraduate Research Programs: A Study of Enabling and Constraining Factors," Research in Higher Education, Springer;Association for Institutional Research, vol. 58(5), pages 520-544, August.
    10. Darrick Yee & Andrew Ho, 2015. "Discreteness Causes Bias in Percentage-Based Comparisons: A Case Study From Educational Testing," The American Statistician, Taylor & Francis Journals, vol. 69(3), pages 174-181, August.
    11. Yan Xia & Yanyun Yang, 2016. "Bias Introduced by Rounding in Multiple Imputation for Ordered Categorical Variables," The American Statistician, Taylor & Francis Journals, vol. 70(4), pages 358-364, October.
    12. Lahiri, Kajal & Pulungan, Zulkarnain, 2007. "Income-related health disparity and its determinants in New York state: racial/ethnic and geographical comparisons," MPRA Paper 21694, University Library of Munich, Germany.
    13. Chae, David H. & Lincoln, Karen D. & Adler, Nancy E. & Syme, S. Leonard, 2010. "Do experiences of racial discrimination predict cardiovascular disease among African American men? The moderating role of internalized negative racial group attitudes," Social Science & Medicine, Elsevier, vol. 71(6), pages 1182-1188, September.

  14. Nicholas J. Horton & Garrett M. Fitzmaurice, 2002. "Maximum likelihood estimation of bivariate logistic models for incomplete responses with indicators of ignorable and non‐ignorable missingness," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(3), pages 281-295, July.

    Cited by:

    1. David Draper & Mark Gittoes, 2004. "Statistical analysis of performance indicators in UK higher education," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 449-474, August.

  15. Nicholas J. Horton & Nan M. Laird, 2001. "Maximum Likelihood Analysis of Logistic Regression Models with Incomplete Covariate Data and Auxiliary Information," Biometrics, The International Biometric Society, vol. 57(1), pages 34-42, March.

    Cited by:

    1. Sinha, Sanjoy K. & Laird, Nan M. & Fitzmaurice, Garrett M., 2010. "Multivariate logistic regression with incomplete covariate and auxiliary information," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2389-2397, November.
    2. Liu, Li & Xiang, Liming, 2019. "Missing covariate data in generalized linear mixed models with distribution-free random effects," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 1-16.

  16. Joseph G. Ibrahim & Stuart R. Lipsitz & Nick Horton, 2001. "Using auxiliary data for parameter estimation with non‐ignorably missing outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 361-373.

    Cited by:

    1. Bindele, Huybrechts F. & Nguelifack, Brice M., 2019. "Generalized signed-rank estimation for regression models with non-ignorable missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 14-33.
    2. Yoshiharu Takagi & Yutaka Kano, 2019. "Bias reduction using surrogate endpoints as auxiliary variables," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 837-852, August.
    3. Dan Jackson & Ian R. White & Morven Leese, 2010. "How much can we learn about missing data?: an exploration of a clinical trial in psychiatry," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(3), pages 593-612, July.
    4. Liang, Hua, 2008. "Generalized partially linear models with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 880-895, May.
    5. Wang Miao & Eric J. Tchetgen Tchetgen, 2016. "On varieties of doubly robust estimators under missingness not at random with a shadow variable," Biometrika, Biometrika Trust, vol. 103(2), pages 475-482.
    6. Baojiang Chen & Xiao-Hua Zhou, 2011. "Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates," Biometrics, The International Biometric Society, vol. 67(3), pages 830-842, September.
    7. Liang, Hua & Su, Haiyan & Zou, Guohua, 2008. "Confidence intervals for a common mean with missing data with applications in an AIDS study," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 546-553, December.

  17. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.

    Cited by:

    1. Hildegard Seidl & Matthias Hunger & Reiner Leidl & Christa Meisinger & Rupert Wende & Bernhard Kuch & Rolf Holle, 2015. "Cost-effectiveness of nurse-based case management versus usual care for elderly patients with myocardial infarction: results from the KORINNA study," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(6), pages 671-681, July.
    2. Ronald B. Mincy & Elia De la Cruz Toledo, 2014. "Unemployment and Child Support Compliance Through the Great Recession," Working Papers 14-01-ff, Princeton University, Woodrow Wilson School of Public and International Affairs, Center for Research on Child Wellbeing..
    3. Li, Daniel H. & Wang, Liqun, 2016. "A weighted simulation-based estimator for incomplete longitudinal data models," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 16-22.
    4. Dardas, Anastassios Z. & Williams, Allison & Scott, Darren, 2020. "Carer-employees’ travel behaviour: Assisted-transport in time and space," Journal of Transport Geography, Elsevier, vol. 82(C).
    5. Geronimi, J. & Saporta, G., 2017. "Variable selection for multiply-imputed data with penalized generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 103-114.
    6. Ben F M Wijnen & Karin Pos & Eva Velthorst & Frederike Schirmbeck & Hoi Yau Chan & Lieuwe de Haan & Mark van der Gaag & Silvia M A A Evers & Filip Smit, 2018. "Economic evaluation of brief cognitive behavioural therapy for social activation in recent-onset psychosis," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-16, November.
    7. Patrick Royston & John B. Carlin & Ian R. White, 2009. "Multiple imputation of missing values: New features for mim," Stata Journal, StataCorp LP, vol. 9(2), pages 252-264, June.
    8. Hamid Heidarian Miri & Jafar Hassanzadeh & Abdolreza Rajaeefard & Majid Mirmohammadkhani & Kambiz Ahmadi Angali, 2016. "Multiple Imputation to Correct for Nonresponse Bias: Application in Non-communicable Disease Risk Factors Survey," Global Journal of Health Science, Canadian Center of Science and Education, vol. 8(1), pages 133-133, January.
    9. Adnan Efendic & Tomasz Marek Mickiewicz & Anna Rebmann, 2013. "Growth Aspirations and Social Capital: Young Firms in a Post-Conflict Environment," UCL SSEES Economics and Business working paper series 122, UCL School of Slavonic and East European Studies (SSEES).
    10. Jing Dai & Stefan Sperlich & Walter Zucchini, 2016. "A Simple Method for Predicting Distributions by Means of Covariates with Examples from Poverty and Health Economics," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 152(1), pages 49-80, January.
    11. Jing Dai & Stefan Sperlich & Walter Zucchini, 2011. "Estimating and Predicting Household Expenditures and Income Distributions," MAGKS Papers on Economics 201147, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    12. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52.
    13. Giorgio Calzolari & Laura Neri, 2010. "The Method of Simulated Scores for Estimating Multinormal Regression Models with Missing Values," Econometrics Working Papers Archive wp2010_01, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    14. Paredes-Gazquez, Juan Diego & Rodriguez-Fernandez, José Miguel & de la Cuesta-Gonzalez, Marta, 2016. "Measuring corporate social responsibility using composite indices: Mission impossible? The case of the electricity utility industry," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 19(1), pages 142-153.
    15. Yijie Zhou & Francesca Dominici & Thomas A. Louis, 2010. "Racial disparities in risks of mortality in a sample of the US Medicare population," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 319-339, March.
    16. Hua Yun Chen & Hui Xie & Yi Qian, 2011. "Multiple Imputation for Missing Values through Conditional Semiparametric Odds Ratio Models," Biometrics, The International Biometric Society, vol. 67(3), pages 799-809, September.
    17. Kristian Kleinke & Jost Reinecke, 2013. "Multiple imputation of incomplete zero-inflated count data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(3), pages 311-336, August.
    18. Atanu Bhattacharjee, 2020. "Estimation of Treatment Effect with Missing Observations for Three Arms and Three Periods Crossover Clinical Trials," Annals of Data Science, Springer, vol. 7(3), pages 447-460, September.
    19. Opoku-Agyemang, Kweku A., 2017. "A Human-Computer Interaction Approach for Integrity in Economics," SocArXiv ra3cs, Center for Open Science.
    20. Consentino, Fabrizio & Claeskens, Gerda, 2010. "Order selection tests with multiply imputed data," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2284-2295, October.
    21. Aderiana Mutheu Mbandi & Jan R. Böhnke & Dietrich Schwela & Harry Vallack & Mike R. Ashmore & Lisa Emberson, 2019. "Estimating On-Road Vehicle Fuel Economy in Africa: A Case Study Based on an Urban Transport Survey in Nairobi, Kenya," Energies, MDPI, Open Access Journal, vol. 12(6), pages 1-28, March.
    22. Paul Zhang, 2005. "Multiple imputation of missing data with ante-dependence covariance structure," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(2), pages 141-155.
    23. Robert Rieg, 2019. "Selbstständigkeit von Bilanzbuchhaltern und Controllern: Eine empirische Untersuchung zu Einkommen und Determinanten," ZfKE – Zeitschrift für KMU und Entrepreneurship, Duncker & Humblot, Berlin, vol. 67(1), pages 35-66.
    24. Janet MacNeil Vroomen & Iris Eekhout & Marcel G. Dijkgraaf & Hein van Hout & Sophia E. de Rooij & Martijn W. Heymans & Judith E. Bosmans, 2016. "Multiple imputation strategies for zero-inflated cost data in economic evaluations: which method works best?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(8), pages 939-950, November.
    25. Víctor Amor-Esteban & Mª-Purificación Galindo-Villardón & Isabel-María García-Sánchez, 2019. "A Multivariate Proposal for a National Corporate Social Responsibility Practices Index (NCSRPI) for International Settings," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(2), pages 525-560, June.
    26. Ulrich Rendtel, 2006. "The 2005 Plenary Meeting on ‘‘Missing Data and Measurement Error’’," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(4), pages 493-499, December.
    27. Ringle, Jay L. & Huefner, Jonathan C. & James, Sigrid & Pick, Robert & Thompson, Ronald W., 2012. "12-month follow-up outcomes for youth departing an integrated residential continuum of care," Children and Youth Services Review, Elsevier, vol. 34(4), pages 675-679.
    28. Echaniz, Eneko & Ho, Chinh Q. & Rodriguez, Andres & dell'Olio, Luigi, 2019. "Comparing best-worst and ordered logit approaches for user satisfaction in transit services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 752-769.
    29. Andrew Briggs & Taane Clark & Jane Wolstenholme & Philip Clarke, 2003. "Missing.... presumed at random: cost‐analysis of incomplete data," Health Economics, John Wiley & Sons, Ltd., vol. 12(5), pages 377-392, May.
    30. Gabriele Beissel Durrant, 2009. "Imputation Methods for Handling Item-Nonresponse in the Social Sciences: A Methodological Review," Working Papers id:2007, eSocialSciences.
    31. Susanne Rässler & Regina Riphahn, 2006. "Survey item nonresponse and its treatment," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 217-232, March.
    32. David K. Blough & Scott Ramsey & Sean D. Sullivan & Roger Yusen, 2009. "The impact of using different imputation methods for missing quality of life scores on the estimation of the cost‐effectiveness of lung‐volume‐reduction surgery," Health Economics, John Wiley & Sons, Ltd., vol. 18(1), pages 91-101, January.
    33. Kristian Kleinke & Mark Stemmler & Jost Reinecke & Friedrich Lösel, 2011. "Efficient ways to impute incomplete panel data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 351-373, December.

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