Estimation of Conditional Distributions and Rank-Tracking Probabilities with Time-Varying Transformation Models

• Activity Number: 495
• Type: Topic Contributed
• Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Xin Tian*+
• Companies: NHLBI/NIH
• Keywords: Conditional distribution function ; Longitudinal data ; smoothing method ; transformation models ; rank-tracking probability

Abstract

An 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 on 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. Application of our models and estimation method is demonstrated through an epidemiological study of childhood growth and blood pressure.