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

DOI10.1080/01621459.2013.808949OpenAlexW2138813263MaRDI QIDQ2861810

Wu, Colin O., Xin Tian

Publication date: 11 November 2013

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2013.808949

zbMATH Keywords

longitudinal dataproportional odds modelconditional distribution functiontracking ability two-step smoothing method

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (13)

Clustering of longitudinal interval-valued data via mixture distribution under covariance separability ⋮ The Influence of Biostatistics at the National Heart, Lung, and Blood Institute ⋮ Estimation and inference on the joint conditional distribution for bivariate longitudinal data using Gaussian copula ⋮ Nonparametric Estimation of Time-Variant Parametric Models with Application to Cross-Sectional Data ⋮ Time-varying coefficient model estimation through radial basis functions ⋮ Rank dynamics for functional data ⋮ Local Box–Cox transformation on time-varying parametric models for smoothing estimation of conditional CDF with longitudinal data ⋮ Analysis of multivariate longitudinal data using dynamic lasso-regularized copula models with application to large pediatric cardiovascular studies ⋮ Risk-predictive probabilities and dynamic nonparametric conditional quantile models for longitudinal analysis ⋮ Nonparametric estimation of conditional distribution functions with longitudinal data and time-varying parametric models ⋮ Probabilistic Time Series Forecasts with Autoregressive Transformation Models ⋮ Transformation boosting machines ⋮ Most Likely Transformations

Uses Software

• ElemStatLearn

Cites Work

• Quantile regression in partially linear varying coefficient models
• Models for discrete longitudinal data.
• Joint modelling of paired sparse functional data using principal components
• Analysis of transformation models with censored data
• Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data
• Using a Box–Cox Transformation in the Analysis of Longitudinal Data with Incomplete Responses
• Nonparametric estimation for time-varying transformation models with longitudinal data

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