Nonparametric estimation of conditional distributions and rank-tracking probabilities with time-varying transformation models in longitudinal studies
Colin O. Wu
National Heart, Lung and Blood Institute
National Institutes of Health
Engr 4201 (Computer science conference room)
4400 University Drive, Fairfax, VA 22030
Time: 11:00 A.M. - 12:00 P.M.
Date: Friday, Mar 22, 2013
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 cannot 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 cannot 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)