A Bayesian model averaging approach to analyzing categorical data with nonignorable nonresponse
In many surveys, the goal is to estimate the proportion of the population with a certain characteristic of interest. This estimation problem is often complicated by survey nonresponse and the difficulty in modeling the nonresponse mechanism. In this paper, a new method is developed for analyzing categorical data with nonresponse when there is uncertainty about ignorability, which incorporates the idea that there are many a priori plausible ignorable and nonignorable nonresponse models. A class of saturated submodels of the full, nonidentifiable likelihood, containing models which have mixtures of ignorable and nonignorable components is considered, and Bayesian averaging is used to incorporate model uncertainty. This approach is then extended by using uniform priors on model components which do not fit into the partition structure. This method is illustrated using data from the 2000 Accuracy and Coverage Evaluation Survey. A simulation study is used to evaluate the performance of this method and to compare it to other popular nonignorable Bayesian models. The results of the simulation study show that the proposed method generates point estimates which can have reduced mean squared error, and credible intervals which are often, on average, narrower, and which contain the true value of the parameter more frequently, as compared to other nonignorable models, and hence provides a better method for quantifying the additional uncertainty due to the missing data.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 57 (2013)
Issue (Month): 1 ()
|Contact details of provider:|| Web page: http://www.elsevier.com/locate/csda|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Gustafson, Paul, 2009. "What Are the Limits of Posterior Distributions Arising From Nonidentified Models, and Why Should We Care?," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1682-1695.
- Chen, Song Xi & Tang, Cheng Yong & Mule, Vincent T., 2010. "Local Post-Stratification in Dual System Accuracy and Coverage Evaluation for the U.S. Census," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 105-119.
- Geert Molenberghs & Caroline Beunckens & Cristina Sotto & Michael G. Kenward, 2008. "Every missingness not at random model has a missingness at random counterpart with equal fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 371-388.
- Nandram B. & Choi J.W., 2002. "Hierarchical Bayesian Nonresponse Models for Binary Data From Small Areas With Uncertainty About Ignorability," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 381-388, June.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:600-614. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
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 references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.