Model Robust Regression Based on Generalized Estimating Equations
| dc.contributor.author | Clark, Seth K. | en |
| dc.contributor.committeecochair | Birch, Jeffrey B. | en |
| dc.contributor.committeecochair | Schabenberger, Oliver | en |
| dc.contributor.committeemember | Terrell, George R. | en |
| dc.contributor.committeemember | Ye, Keying | en |
| dc.contributor.committeemember | Anderson-Cook, Christine M. | en |
| dc.contributor.department | Statistics | en |
| dc.date.accessioned | 2014-03-14T20:08:45Z | en |
| dc.date.adate | 2002-04-04 | en |
| dc.date.available | 2014-03-14T20:08:45Z | en |
| dc.date.issued | 2002-03-29 | en |
| dc.date.rdate | 2003-04-04 | en |
| dc.date.sdate | 2002-04-02 | en |
| dc.description.abstract | One form of model robust regression (MRR) predicts mean response as a convex combination of a parametric and a nonparametric prediction. MRR is a semiparametric method by which an incompletely or an incorrectly specified parametric model can be improved through adding an appropriate amount of a nonparametric fit. The combined predictor can have less bias than the parametric model estimate alone and less variance than the nonparametric estimate alone. Additionally, as shown in previous work for uncorrelated data with linear mean function, MRR can converge faster than the nonparametric predictor alone. We extend the MRR technique to the problem of predicting mean response for clustered non-normal data. We combine a nonparametric method based on local estimation with a global, parametric generalized estimating equations (GEE) estimate through a mixing parameter on both the mean scale and the linear predictor scale. As a special case, when data are uncorrelated, this amounts to mixing a local likelihood estimate with predictions from a global generalized linear model. Cross-validation bandwidth and optimal mixing parameter selectors are developed. The global fits and the optimal and data-driven local and mixed fits are studied under no/some/substantial model misspecification via simulation. The methods are then illustrated through application to data from a longitudinal study. | en |
| dc.description.degree | Ph. D. | en |
| dc.identifier.other | etd-04022002-161238 | en |
| dc.identifier.sourceurl | http://scholar.lib.vt.edu/theses/available/etd-04022002-161238/ | en |
| dc.identifier.uri | http://hdl.handle.net/10919/26588 | en |
| dc.publisher | Virginia Tech | en |
| dc.relation.haspart | 0Dissertation.pdf | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Local Model | en |
| dc.subject | Nonparametric Regression | en |
| dc.subject | Semiparametric | en |
| dc.subject | Model Misspecification | en |
| dc.title | Model Robust Regression Based on Generalized Estimating Equations | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Statistics | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
Files
Original bundle
1 - 1 of 1