Search Results - "Expectation conditional-maximization algorithm"

A practical model‐based segmentation approach for improved activation detection in single‐subject functional magnetic resonance imaging studies by Chen, Wei‐Chen, Maitra, Ranjan

“…‐signal contexts and single‐subject studies. Accurate activation detection can be guided by the fact that very few voxels are, in reality, truly activated…”
Get full text

Lifetime prognostics for deteriorating systems with time-varying random jumps by Zhang, Jian-Xun, Hu, Chang-Hua, He, Xiao, Si, Xiao-Sheng, Liu, Yang, Zhou, Dong-Hua

“…•A degradation model with time-varying random jumps is presented.•The approximated analytical lifetime of the proposed model is derived.•A two-step parameters…”
Get full text

Multivariate t nonlinear mixed-effects models for multi-outcome longitudinal data with missing values by Wang, Wan-Lun, Lin, Tsung-I

“…‐subject errors are assumed to be normally distributed for mathematical tractability and computational simplicity…”
Get full text

Estimation in multivariate t$t$ linear mixed models for longitudinal data with multiple outputs: Application to PBCseq data analysis by Taavoni, Mozhgan, Arashi, Mohammad

“…In many biomedical studies or clinical trials, we have data with more than one response variable on the same subject repeatedly measured over time…”
Get full text

Multivariate generalized hyperbolic laws for modeling financial log‐returns: Empirical and theoretical considerations by Fotopoulos, Stergios B., Paparas, Alex, Jandhyala, Venkata K.

“…Summary The aim of this study is to consider the multivariate generalized hyperbolic (MGH) distribution for modeling financial log‐returns. Beginning with the…”
Get full text

A robust threshold t linear mixed model for subgroup identification using multivariate T distributions by Zhang, Rui, Qin, Guoyou, Tu, Dongsheng

“… In this paper, we propose a robust subgroup identification method for longitudinal data by developing a robust threshold t linear mixed-effects model, where random effects and within-subject errors…”
Get full text

Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values by Wang, Wan-Lun

“… Under a missing at random mechanism, a pseudo-data version of the alternating expectation-conditional maximization algorithm is developed to carry out maximum likelihood estimation and impute missing…”
Get full text

Estimation in multivariate linear mixed models for longitudinal data with multiple outputs: Application to PBCseq data analysis by Taavoni, Mozhgan, Arashi, Mohammad

“…In many biomedical studies or clinical trials, we have data with more than one response variable on the same subject repeatedly measured over time…”
Get full text