Statistics Seminar
| Date/Time: | Monday, 07 Nov 2011 from 4:10 pm to 5:00 pm |
|---|---|
| Location: | 3105 Snedecor |
| Cost: | Free |
| URL: | www.stat.iastate.edu |
| Contact: | Jeanette La Grange |
| Phone: | 515-294-3440 |
| Channel: | College of Liberal Arts and Sciences |
| Categories: | Lectures |
| Actions: | Download iCal/vCal | Email Reminder |
We develop two Bayesian model-based estimators of finite population quantiles for continuous survey variables, defined using spline-based models, in unequal probability sampling. The first method is to estimate cumulative distribution functions of the continuous survey variable by fitting a number of probit penalized spline regression models on the inclusion probabilities. The finite population quantiles are then obtained by inverting the estimated distribution function. This method is quite computationally demanding. The second method predicts non-sampled values assuming a smoothly-varying relationship between the continuous survey variable and the probability of inclusion, by modeling both the mean function and the variance function using splines. The two Bayesian spline-model-based estimators yield desirable balance between robustness and efficiency. Simulation studies show that both methods yield smaller root mean squared errors than the sample-weighted estimator and the ratio and difference estimators described by Rao, Kovar, and Mantel (1990), and are more robust to model misspecification than the Chambers and Dunstan's model-based estimator (1986). When sample size is small, the 95% credible intervals of the two new methods have closer to the nominal level confidence coverage than the sample-weighted estimator.
Bio: Qixuan Chen received her PhD in Biostatistics from the University of Michigan in 2009 and is now an Assistant Professor in the Department of Biostatistics at Columbia University. Her research interests are in survey statistics, missing data, and novel application of statistical methods in environmental epidemiology, mental health, and pediatric research.