6 papers
PCA score regression: the art of losing power
Yu Lu, Nidhi Pai, Erjia Cui +1
The regression of principal component scores (RPCS) on covariates is a widely used analytic approach to detect and test for associations between functional measurements and study p…
Quantifying Time-Varying Physical Activity Intervention Effects via Functional Regression
Nidhi Pai, Yu Lu, Kristin A. Linn +1
Physical activity (PA) intervention studies often collect repeated intensity measurements over long observation periods. Quantifying the variation in intervention effects over the…
Group-Sparse Smoothing for Longitudinal Models with Time-Varying Coefficients
Yu Lu, Tianni Zhang, Yuyao Wang +1
Longitudinal associations may vary over time, yet allowing every regression effect to be dynamic can inflate estimation variance and obscure interpretable structure. We develop tim…
Adaptive Penalized Doubly Robust Regression for Longitudinal Data
Yuyao Wang, Yu Lu, Tianni Zhang +1
Longitudinal data often involve heterogeneity, sparse signals, and contamination from response outliers or high-leverage observations especially in biomedical science. Existing met…
Block Empirical Likelihood Inference for Longitudinal Generalized Partially Linear Single-Index Models
Tianni Zhang, Yuyao Wang, Yu Lu +1
Generalized partially linear single-index models (GPLSIMs) provide a flexible and interpretable semiparametric framework for longitudinal outcomes by combining a low-dimensional pa…
Generalized Conditional Functional Principal Component Analysis
Yu Lu, Xinkai Zhou, Erjia Cui +4
We propose generalized conditional functional principal components analysis (GC-FPCA) for the joint modeling of the fixed and random effects of non-Gaussian functional outcomes. Th…