40 citations · 58 across the 5 of their papers we have counts for
5 papers
Understanding the dynamic impact of COVID-19 through competing risk modeling with bivariate varying coefficients
Wenbo Wu, John D. Kalbfleisch, Jeremy M. G. Taylor +2
The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound impact on patients with end-stage renal disease relying on kidney dialysis to sustain their lives. Motivated…
Kernel estimation of bivariate time-varying coefficient model for longitudinal data with terminal event
Yue Wang, Bin Nan, Jack D. Kalbfleisch
We propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The ti…
Accounting for total variation and robustness in profiling health care providers
Lu Xia, Kevin He, Yanming Li +1
Monitoring outcomes of health care providers, such as patient deaths, hospitalizations and hospital readmissions, helps in assessing the quality of health care. We consider a large…
Block-Conditional Missing at Random Models for Missing Data
Yan Zhou, Roderick J. A. Little, John D. Kalbfleisch
Two major ideas in the analysis of missing data are (a) the EM algorithm [Dempster, Laird and Rubin, J. Roy. Statist. Soc. Ser. B 39 (1977) 1--38] for maximum likelihood (ML) estim…
Asymptotic theory for the semiparametric accelerated failure time model with missing data
Bin Nan, John D. Kalbfleisch, Menggang Yu
We consider a class of doubly weighted rank-based estimating methods for the transformation (or accelerated failure time) model with missing data as arise, for example, in case-coh…