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Nonparametric Estimation of Conditional Distributions and Rank-Tracking Probabilities With Time-Varying Transformation Models in Longitudinal Studies

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  • Colin O. Wu
  • Xin Tian

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 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 an epidemiological study of childhood growth and blood pressure. Supplementary materials for this article are available online.

Suggested Citation

  • Colin O. Wu & Xin Tian, 2013. "Nonparametric Estimation of Conditional Distributions and Rank-Tracking Probabilities With Time-Varying Transformation Models in Longitudinal Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 971-982, September.
    • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:503:p:971-982
    DOI: 10.1080/01621459.2013.808949
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    References listed on IDEAS

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    1. Colin Wu & Xin Tian & Jarvis Yu, 2010. "Nonparametric estimation for time-varying transformation models with longitudinal data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(2), pages 133-147.
    2. Hall, Peter & Wolff, Rodney C. L. & Yao, Qiwei, 1999. "Methods for estimating a conditional distribution function," LSE Research Online Documents on Economics 6631, London School of Economics and Political Science, LSE Library.
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    Cited by:

    1. Ming Xiong & Ao Yuan & Hong-Bin Fang & Colin O. Wu & Ming T. Tan, 2022. "Estimation and Hypothesis Test for Mean Curve with Functional Data by Reproducing Kernel Hilbert Space Methods, with Applications in Biostatistics," Mathematics, MDPI, vol. 10(23), pages 1-17, December.
    2. Minjung Kwak, 2017. "Estimation and inference of the joint conditional distribution for multivariate longitudinal data using nonparametric copulas," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(3), pages 491-514, July.
    3. Chen, Yaqing & Dawson, Matthew & Müller, Hans-Georg, 2020. "Rank dynamics for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).

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