Open Access. Powered by Scholars. Published by Universities.®

Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Marginal Hazards Model For Multivariate Failure Time Data With Auxiliary Covariates, Zhaozhi Fan, Xiao-Feng Wang Sep 2009

Marginal Hazards Model For Multivariate Failure Time Data With Auxiliary Covariates, Zhaozhi Fan, Xiao-Feng Wang

Xiaofeng Wang

A marginal hazards model of multivariate failure times has been developed based on the ‘working independence’ assumption [L.J. Wei, D.Y. Lin, and L. Wessfeld, Regression analysis of multivariate incomplete failure time data by modeling marginal distributions, J. Amer. Statist. Assoc. 84 (1989), pp. 1065–1073.]. In this article, we study the marginal hazards model of multivariate failure times with continuous auxiliary covariates. We consider the case of common baseline hazards for subjects from the same clusters. We extend the kernel smoothing procedure of Zhou and Wang [H. Zhou and C.Y. Wang, Failure time regression with continuous covariates measured with error, J. …


Assessing Time-Dependent Association Between Scalp Eeg And Muscle Activation: A Functional Random-Effects Model Approach, Xiao-Feng Wang, Qi Yang, Zhaozhi Fan, Chang-Kai Sun, Guang H. Yue Feb 2009

Assessing Time-Dependent Association Between Scalp Eeg And Muscle Activation: A Functional Random-Effects Model Approach, Xiao-Feng Wang, Qi Yang, Zhaozhi Fan, Chang-Kai Sun, Guang H. Yue

Xiaofeng Wang

This study investigates time-dependent associations between source strength estimated from high-density scalp electroencephalogram (EEG) and force of voluntary handgrip contraction at different intensity levels. We first estimate source strength from raw EEG signals collected during voluntary muscle contractions at different levels and then propose a functional random-effects model approach in which both functional fixed effects and functional random-effects are considered for the data. Two estimation procedures for the functional model are discussed. The first estimation procedure is a two-step method which involves no iterations. It can flexibly use different smoothing methods and smoothing parameters. The second estimation procedure benefits from …