IDEAS home Printed from https://ideas.repec.org/r/bla/jorssb/v70y2008i2p371-388.html
   My bibliography  Save this item

Every missingness not at random model has a missingness at random counterpart with equal fit

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Yekun Qin & Shanminhui Yin & Fang Liu, 2024. "Navigating Criminal Responsibility in the Digital Marketplace: Implications of Network-Neutral Help Behavior and Beyond-5G Networks in E-Commerce Transactions," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 10667-10695, September.
  2. Janicki, Ryan & Malec, Donald, 2013. "A Bayesian model averaging approach to analyzing categorical data with nonignorable nonresponse," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 600-614.
  3. Rhoads Christopher H., 2012. "Problems with Tests of the Missingness Mechanism in Quantitative Policy Studies," Statistics, Politics and Policy, De Gruyter, vol. 3(1), pages 1-25, March.
  4. Jeon, Saebom & Kwon, Tae Yeon & Park, Yousung, 2019. "Variable-based missing mechanism for an incomplete contingency table with unit missingness," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 90-96.
  5. Yuriko Takeda & Toshihiro Misumi & Kouji Yamamoto, 2022. "Joint Models for Incomplete Longitudinal Data and Time-to-Event Data," Mathematics, MDPI, vol. 10(19), pages 1-7, October.
  6. Trias Wahyuni Rakhmawati & Geert Molenberghs & Geert Verbeke & Christel Faes, 2016. "Local influence diagnostics for incomplete overdispersed longitudinal counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1722-1737, July.
  7. D M Farewell & R M Daniel & S R Seaman, 2022. "Missing at random: a stochastic process perspective [Contribution to the discussion of ‘Longitudinal data with dropout: Objectives, assumptions and a proposal’ by P. J. Diggle, D. Farewell and R. H," Biometrika, Biometrika Trust, vol. 109(1), pages 227-241.
  8. Bian, Yuan & Yi, Grace Y. & He, Wenqing, 2024. "A unified framework of analyzing missing data and variable selection using regularized likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
  9. Maria Marino & Marco Alfó, 2015. "Latent drop-out based transitions in linear quantile hidden Markov models for longitudinal responses with attrition," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 483-502, December.
  10. Marco Doretti & Martina Raggi & Elena Stanghellini, 2022. "Exact parametric causal mediation analysis for a binary outcome with a binary mediator," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 87-108, March.
  11. Kim, Seongyong & Park, Yousung & Kim, Daeyoung, 2015. "On missing-at-random mechanism in two-way incomplete contingency tables," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 196-203.
  12. Caroline Beunckens & Cristina Sotto & Geert Molenberghs & Geert Verbeke, 2009. "A multifaceted sensitivity analysis of the Slovenian public opinion survey data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(2), pages 171-196, May.
  13. Marco Doretti & Sara Geneletti & Elena Stanghellini, 2018. "Missing Data: A Unified Taxonomy Guided by Conditional Independence," International Statistical Review, International Statistical Institute, vol. 86(2), pages 189-204, August.
  14. Yuzhe Liu & Vanathi Gopalakrishnan, 2017. "An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data," Data, MDPI, vol. 2(1), pages 1-15, January.
  15. Hairu Wang & Zhiping Lu & Yukun Liu, 2023. "Score test for missing at random or not under logistic missingness models," Biometrics, The International Biometric Society, vol. 79(2), pages 1268-1279, June.
  16. Heyna, Philipp, 2024. "Can TikTok Drive Support for Populist Radical Right Parties? Causal Evidence From Germany," OSF Preprints yju9n, Center for Open Science.
  17. Wan-Lun Wang & Min Liu & Tsung-I Lin, 2017. "Robust skew-t factor analysis models for handling missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 649-672, November.
  18. Yuan, Ke-Hai, 2009. "Normal distribution based pseudo ML for missing data: With applications to mean and covariance structure analysis," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1900-1918, October.
  19. Betsy J. Feldman & Sophia Rabe-Hesketh, 2012. "Modeling Achievement Trajectories When Attrition Is Informative," Journal of Educational and Behavioral Statistics, , vol. 37(6), pages 703-736, December.
  20. Roula Tsonaka & Dimitris Rizopoulos & Geert Verbeke & Emmanuel Lesaffre, 2010. "Nonignorable Models for Intermittently Missing Categorical Longitudinal Responses," Biometrics, The International Biometric Society, vol. 66(3), pages 834-844, September.
  21. Andrew T. Karl & Yan Yang & Sharon L. Lohr, 2013. "A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects," Journal of Educational and Behavioral Statistics, , vol. 38(6), pages 577-603, December.
  22. Bunouf, Pierre & Molenberghs, Geert & Grouin, Jean-Marie & Thijs, Herbert, 2015. "A SAS Program Combining R Functionalities to Implement Pattern-Mixture Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i08).
  23. Ying Yuan & Guosheng Yin, 2010. "Bayesian Quantile Regression for Longitudinal Studies with Nonignorable Missing Data," Biometrics, The International Biometric Society, vol. 66(1), pages 105-114, March.
  24. A.Y. Kombo & H. Mwambi & G. Molenberghs, 2017. "Multiple imputation for ordinal longitudinal data with monotone missing data patterns," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 270-287, January.
  25. Margarita Moreno-Betancur & Grégoire Rey & Aurélien Latouche, 2015. "Direct likelihood inference and sensitivity analysis for competing risks regression with missing causes of failure," Biometrics, The International Biometric Society, vol. 71(2), pages 498-507, June.
  26. D. M. Farewell & C. Huang & V. Didelez, 2017. "Ignorability for general longitudinal data," Biometrika, Biometrika Trust, vol. 104(2), pages 317-326.
  27. Kott Phillip S. & Liao Dan, 2018. "Calibration Weighting for Nonresponse with Proxy Frame Variables (So that Unit Nonresponse Can Be Not Missing at Random)," Journal of Official Statistics, Sciendo, vol. 34(1), pages 107-120, March.
  28. Rianne Margaretha Schouten & Gerko Vink, 2021. "The Dance of the Mechanisms: How Observed Information Influences the Validity of Missingness Assumptions," Sociological Methods & Research, , vol. 50(3), pages 1243-1258, August.
  29. Morten Overgaard & Stefan Nygaard Hansen, 2021. "On the assumption of independent right censoring," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1234-1255, December.
  30. Yordan P Raykov & Alexis Boukouvalas & Fahd Baig & Max A Little, 2016. "What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-28, September.
  31. Christos Thomadakis & Loukia Meligkotsidou & Nikos Pantazis & Giota Touloumi, 2019. "Longitudinal and time‐to‐drop‐out joint models can lead to seriously biased estimates when the drop‐out mechanism is at random," Biometrics, The International Biometric Society, vol. 75(1), pages 58-68, March.
  32. Brick J. Michael, 2013. "Unit Nonresponse and Weighting Adjustments: A Critical Review," Journal of Official Statistics, Sciendo, vol. 29(3), pages 329-353, June.
  33. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2019. "Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data," Econometrics, MDPI, vol. 7(3), pages 1-27, September.
  34. Brenden Bishop & Minjeong Jeon, 2016. "Book Review," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 1164-1167, December.
  35. Hines, R.J. O'Hara & Hines, W.G.S., 2010. "Indices for covariance mis-specification in longitudinal data analysis with no missing responses and with MAR drop-outs," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 806-815, April.
  36. Shu Xu & Shelley A. Blozis, 2011. "Sensitivity Analysis of Mixed Models for Incomplete Longitudinal Data," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 237-256, April.
  37. Anna Ivanova & Geert Molenberghs & Geert Verbeke, 2017. "Mechanism for missing data incorporated in joint modelling of ordinal responses," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(5), pages 1049-1064, November.
  38. Daniel, Rhian M. & Kenward, Michael G., 2012. "A method for increasing the robustness of multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1624-1643.
  39. Geert Molenberghs, 2012. "Discussion Contribution to 091037PR4 (Ghosh, Taylor, and Sargent)," Biometrics, The International Biometric Society, vol. 68(1), pages 233-235, March.
  40. Spagnoli, Alessandra & Henderson, Robin & Boys, Richard J. & Houwing-Duistermaat, Jeanine J., 2011. "A hidden Markov model for informative dropout in longitudinal response data with crisis states," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 730-738, July.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.