Semiparametric approach for non-monotone missing covariates in a parametric regression model
Author
Abstract
Suggested Citation
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- 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.
- Hua Yun Chen, 2004. "Nonparametric and Semiparametric Models for Missing Covariates in Parametric Regression," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1176-1189, December.
- Stanislav Kolenikov & Gustavo Angeles, 2009. "Socioeconomic Status Measurement With Discrete Proxy Variables: Is Principal Component Analysis A Reliable Answer?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 55(1), pages 128-165, March.
- Chatterjee N. & Chen Y-H. & Breslow N.E., 2003. "A Pseudoscore Estimator for Regression Problems With Two-Phase Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 158-168, January.
- Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
- Joseph G. Ibrahim & Ming-Hui Chen & Stuart R. Lipsitz, 1999. "Monte Carlo EM for Missing Covariates in Parametric Regression Models," Biometrics, The International Biometric Society, vol. 55(2), pages 591-596, June.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Zhuoer Sun & Suojin Wang, 2019. "Semiparametric estimation in regression with missing covariates using single-index models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1201-1232, October.
- Timothy Reese & Majid Mojirsheibani, 2017. "On the $$L_p$$ L p norms of kernel regression estimators for incomplete data with applications to classification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 81-112, March.
- Eric Han & Majid Mojirsheibani, 2021. "On histogram-based regression and classification with incomplete data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 635-662, July.
- Yang Zhao, 2021. "Semiparametric model for regression analysis with nonmonotone missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 461-475, June.
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.- Zhiwei Zhang & Howard Rockette, 2006. "Semiparametric Maximum Likelihood for Missing Covariates in Parametric Regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(4), pages 687-706, December.
- Yang Zhao, 2021. "Semiparametric model for regression analysis with nonmonotone missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 461-475, June.
- 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.
- Gerda Claeskens & Fabrizio Consentino, 2008. "Variable Selection with Incomplete Covariate Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1062-1069, December.
- Chen, Qingxia & Ibrahim, Joseph G. & Chen, Ming-Hui & Senchaudhuri, Pralay, 2008. "Theory and inference for regression models with missing responses and covariates," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1302-1331, July.
- Timothy Reese & Majid Mojirsheibani, 2017. "On the $$L_p$$ L p norms of kernel regression estimators for incomplete data with applications to classification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 81-112, March.
- 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.
- Hongtu Zhu & Joseph G. Ibrahim & Xiaoyan Shi, 2009. "Diagnostic Measures for Generalized Linear Models with Missing Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 686-712, December.
- James Y. Dai & Michael LeBlanc & Charles Kooperberg, 2009. "Semiparametric Estimation Exploiting Covariate Independence in Two-Phase Randomized Trials," Biometrics, The International Biometric Society, vol. 65(1), pages 178-187, March.
- Lan Huang & Ming-Hui Chen & Joseph G. Ibrahim, 2005. "Bayesian Analysis for Generalized Linear Models with Nonignorably Missing Covariates," Biometrics, The International Biometric Society, vol. 61(3), pages 767-780, September.
- Andrzej S. Kosinski & Huiman X. Barnhart, 2003. "Accounting for Nonignorable Verification Bias in Assessment of Diagnostic Tests," Biometrics, The International Biometric Society, vol. 59(1), pages 163-171, March.
- Nanhua Zhang & Roderick J. Little, 2012. "A Pseudo-Bayesian Shrinkage Approach to Regression with Missing Covariates," Biometrics, The International Biometric Society, vol. 68(3), pages 933-942, September.
- Xiaoyan Shi & Hongtu Zhu & Joseph G. Ibrahim, 2009. "Local Influence for Generalized Linear Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 65(4), pages 1164-1174, December.
- Yang Zhao & Meng Liu, 2021. "Unified approach for regression models with nonmonotone missing at random data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 87-101, March.
- Ib Thomsen & Li-Chun Zhang & Joseph Sexton, 2000. "Markov Chain Generated Profile Likelihood Inference under Generalized Proportional to Size Non-ignorable Non-response," Discussion Papers 274, Statistics Norway, Research Department.
- Ming‐Hui Chen & Joseph G. Ibrahim, 2001. "Maximum Likelihood Methods for Cure Rate Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 57(1), pages 43-52, March.
- Guirong Li & Jiajia Xu & Liying Li & Zhaolei Shi & Hongmei Yi & James Chu & Elena Kardanova & Yanyan Li & Prashant Loyalka & Scott Rozelle, 2020. "The Impacts of Highly Resourced Vocational Schools on Student Outcomes in China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 28(6), pages 125-150, November.
- Shu Yang & Jae Kwang Kim, 2016. "Likelihood-based Inference with Missing Data Under Missing-at-Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 436-454, June.
- Nguimkeu, Pierre & Denteh, Augustine & Tchernis, Rusty, 2019.
"On the estimation of treatment effects with endogenous misreporting,"
Journal of Econometrics, Elsevier, vol. 208(2), pages 487-506.
- Pierre Nguimkeu & Augustine Denteh & Rusty Tchernis, 2017. "On the Estimation of Treatment Effects with Endogenous Misreporting," NBER Working Papers 24117, National Bureau of Economic Research, Inc.
- Pierre Nguimkeu & Augustine Denteh & Rusty Tchernis, 2018. "On the Estimation of Treatment Effects with Endogenous Misreporting," Working Papers 2018-019, Human Capital and Economic Opportunity Working Group.
- Nguimkeu, Pierre & Denteh, Augustine & Tchernis, Rusty, 2018. "On the Estimation of Treatment Effects with Endogenous Misreporting," IZA Discussion Papers 11426, Institute of Labor Economics (IZA).
- Richard Dennis, 2000. "Steps toward identifying central bank policy preferences," Working Paper Series 2000-13, Federal Reserve Bank of San Francisco.
Corrections
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:bla:biomet:v:70:y:2014:i:2:p:299-311. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.