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Department of Statistics Research Seminar

Nonparametric estimation of conditional distributions and rank-tracking probabilities with time-varying transformation models in longitudinal studies

Friday, May 24, 2013 3:00 PM - 5:00 PM

Colin O. Wu, Office of Biostatistics Research, National Heart, Lung and Blood Institute National Institutes of Health Bethesda, MD 20892, wuc@nhlbi.nih.gov

An important objective of longitudinal analysis is to estimate the conditional distributions of an outcome variable through a regression model. The approaches based on modeling the conditional means are not appropriate for this task when the conditional distributions are skewed or can not be approximated by a normal distribution through a known transformation.

We study a class of time-varying transformation models and a two-step smoothing method for the estimation of the conditional distribution functions. Based our models, we propose a rank-tracking probability and a rank-tracking probability ratio to measure the strength of tracking ability of an outcome variable at two different time points. Our models and estimation method can be applied to a wide range of scientific objectives that can not be evaluated by the conditional mean based models.

We derive the asymptotic properties for the two-step local polynomial estimators of the conditional distribution functions. Finite sample properties of our procedures are investigated through a simulation study. Application of our models and estimation method is demonstrated through a large epidemiological study of childhood growth and blood pressure.

*This is the joint work with Xin Tian (OBR/NHLBI)


Kidder Hall (campus map)
350
Free
Judith Burks
1 541 737 3883
judy.burks at oregonstate.edu
College of Science, Statistics Department
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