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Seminar Announcement** * ------------------------------ * * * *Nonparametric estimation of conditional distributions and rank-tracking probabilities with time-varying transformation models in longitudinal studies* *Colin O. Wu * *Office of Biostatistics Research** * *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 *** * * *Abstract* 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) -- Yunpeng Zhao, PhD Assistant Professor Department of Statistics Volgenau School of Engineering <[log in to unmask]> George Mason University Engineering Building, Room 1719, MS 4A7 4400 University Drive Fairfax, VA 22030-4444 Phone: 703-993-1674 Email: [log in to unmask]