Ergänzung fehlender Daten in Umfragen / Imputation of Missing Data in Surveys
Author
Abstract
Suggested Citation
DOI: 10.1515/jbnst-2000-0106
Download full text from publisher
References listed on IDEAS
- repec:wop:ubisop:0072 is not listed on IDEAS
- Eric Schulte Nordholt, 1998. "Imputation: Methods, Simulation Experiments and Practical Examples," International Statistical Review, International Statistical Institute, vol. 66(2), pages 157-180, August.
- J. G. Ibrahim & S. R. Lipsitz & M.‐H. Chen, 1999. "Missing covariates in generalized linear models when the missing data mechanism is non‐ignorable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 173-190.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Zhang, Jing & Wang, Qihua & Kang, Jian, 2020. "Feature screening under missing indicator imputation with non-ignorable missing response," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
- Gerda Claeskens & Fabrizio Consentino, 2008. "Variable Selection with Incomplete Covariate Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1062-1069, December.
- Ramon I. Garcia & Joseph G. Ibrahim & Hongtu Zhu, 2010. "Variable Selection in the Cox Regression Model with Covariates Missing at Random," Biometrics, The International Biometric Society, vol. 66(1), pages 97-104, March.
- Jiang, Depeng & Zhao, Puying & Tang, Niansheng, 2016. "A propensity score adjustment method for regression models with nonignorable missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 98-119.
- Amy H. Herring & Joseph G. Ibrahim & Stuart R. Lipsitz, 2002. "Frailty Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 58(1), pages 98-109, March.
- Seppo Laaksonen, 2003. "Alternative imputation techniques for complex metric variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(9), pages 1009-1020.
- Hongbin Zhang & Lang Wu, 2018. "A non‐linear model for censored and mismeasured time varying covariates in survival models, with applications in human immunodeficiency virus and acquired immune deficiency syndrome studies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1437-1450, November.
- 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.
- Kano, Yutaka & Takai, Keiji, 2011. "Analysis of NMAR missing data without specifying missing-data mechanisms in a linear latent variate model," Journal of Multivariate Analysis, Elsevier, vol. 102(9), pages 1241-1255, October.
- Qingxia Chen & Fan Zhang & Ming-Hui Chen & Xiuyu Julie Cong, 2020. "Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 685-707, October.
- Samiran Sinha & Krishna K. Saha & Suojin Wang, 2014. "Semiparametric approach for non-monotone missing covariates in a parametric regression model," Biometrics, The International Biometric Society, vol. 70(2), pages 299-311, June.
- Amy L. Stubbendick & Joseph G. Ibrahim, 2003. "Maximum Likelihood Methods for Nonignorable Missing Responses and Covariates in Random Effects Models," Biometrics, The International Biometric Society, vol. 59(4), pages 1140-1150, December.
- Jiwei Zhao & Jun Shao, 2015. "Semiparametric Pseudo-Likelihoods in Generalized Linear Models With Nonignorable Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1577-1590, December.
- Dlugosz, Stephan, 2011. "Give missings a chance: Combined stochastic and rule-based approach to improve regression models with mismeasured monotonic covariates without side information," ZEW Discussion Papers 11-013, ZEW - Leibniz Centre for European Economic Research.
- Qingxia Chen & Joseph G. Ibrahim, 2006. "Semiparametric Models for Missing Covariate and Response Data in Regression Models," Biometrics, The International Biometric Society, vol. 62(1), pages 177-184, March.
- Lee, Min Cherng & Mitra, Robin, 2016. "Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 24-38.
- Eleanor McDonnell Feit & Mark A. Beltramo & Fred M. Feinberg, 2010. "Reality Check: Combining Choice Experiments with Market Data to Estimate the Importance of Product Attributes," Management Science, INFORMS, vol. 56(5), pages 785-800, May.
- van den Berg, Gerard J. & van Vuuren, Aico, 2010.
"The effect of search frictions on wages,"
Labour Economics, Elsevier, vol. 17(6), pages 875-885, December.
- Gerard J. van den Berg & Aico van Vuuren, 2002. "The Effect of Search Frictions on Wages," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 C1-2, International Conferences on Panel Data.
- Gerard J. van den Berg & Aico van Vuuren, 2006. "The Effect of Search Frictions on Wages," Tinbergen Institute Discussion Papers 06-077/3, Tinbergen Institute.
- Gérard J, Van den Berg & Aico Van Vuuren, 2003. "The effect of Search Frictions on Wages," Working Papers 2003-29, Center for Research in Economics and Statistics.
- Van den Berg, Gerard & van Vuuren, Aico, 2003. "The Effect of Search Frictions on Wages," CEPR Discussion Papers 3979, C.E.P.R. Discussion Papers.
- van den Berg, Gerard J. & van Vuuren, Aico, 2003. "The Effect of Search Frictions on Wages," IZA Discussion Papers 805, Institute of Labor Economics (IZA).
- van den Berg, Gerard J. & van Vuuren, Aico, 2003. "The effect of search frictions on wages," Working Paper Series 2003:12, IFAU - Institute for Evaluation of Labour Market and Education Policy.
- Puying Zhao & Hui Zhao & Niansheng Tang & Zhaohai Li, 2017. "Weighted composite quantile regression analysis for nonignorable missing data using nonresponse instrument," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(2), pages 189-212, April.
- Hongbin Zhang & Lang Wu, 2019. "An approximate method for generalized linear and nonlinear mixed effects models with a mechanistic nonlinear covariate measurement error model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(4), pages 471-499, May.
More about this item
Keywords
Imputation techniques; data augmentation; hot deck method; missing data mechanism; nonignorable nonresponse;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jns:jbstat:v:220:y:2000:i:1:p:64-94. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.