720 citations · 1.7k across the 23 of their papers we have counts for
10 papers · 1 filter
Neural Networks for Partially Linear Quantile Regression
Qixian Zhong, Jane-Ling Wang
Deep learning has enjoyed tremendous success in a variety of applications but its application to quantile regressions remains scarce. A major advantage of the deep learning approac…
Inverse regression for longitudinal data
Ci-Ren Jiang, Wei Yu, Jane-Ling Wang
Sliced inverse regression (Duan and Li [Ann. Statist. 19 (1991) 505-530], Li [J. Amer. Statist. Assoc. 86 (1991) 316-342]) is an appealing dimension reduction method for regression…
Modeling left-truncated and right-censored survival data with longitudinal covariates
Yu-Ru Su, Jane-Ling Wang
There is a surge in medical follow-up studies that include longitudinal covariates in the modeling of survival data. So far, the focus has been largely on right-censored survival d…
Robust functional principal components: A projection-pursuit approach
Juan Lucas Bali, Graciela Boente, David E. Tyler +1
In many situations, data are recorded over a period of time and may be regarded as realizations of a stochastic process. In this paper, robust estimators for the principal componen…
Functional single index models for longitudinal data
Ci-Ren Jiang, Jane-Ling Wang
A new single-index model that reflects the time-dynamic effects of the single index is proposed for longitudinal and functional response data, possibly measured with errors, for bo…
Functional linear regression via canonical analysis
Guozhong He, Hans-Georg Müller, Jane-Ling Wang +1
We study regression models for the situation where both dependent and independent variables are square-integrable stochastic processes. Questions concerning the definition and exis…
Covariate adjusted functional principal components analysis for longitudinal data
Ci-Ren Jiang, Jane-Ling Wang
Classical multivariate principal component analysis has been extended to functional data and termed functional principal component analysis (FPCA). Most existing FPCA approaches do…
The central limit theorem under random truncation
Winfried Stute, Jane-Ling Wang
Under left truncation, data are observed only when . Usually, the distribution function of the is the target of interest. In this paper, we study…
Properties of principal component methods for functional and longitudinal data analysis
Peter Hall, Hans-Georg Müller, Jane-Ling Wang
The use of principal component methods to analyze functional data is appropriate in a wide range of different settings. In studies of ``functional data analysis,'' it has often bee…
Functional linear regression analysis for longitudinal data
Fang Yao, Hans-Georg Müller, Jane-Ling Wang
We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariat…