Ying Guo
hc-ICA: Hierarchical covariate-adjusted  ICA model

     Shi, R. and Guo, Y (2016+). Modeling Covariate Effects in Group Independent Component Analysis with Application to Functional Magnetic Resonance Imaging. Annals of Applied Statistics. (Accepted). An earlier version of the paper was selected for Best Student Paper Award, American Statistical Association (ASA) Statistics in Imaging Section.

 This paper proposes the hc-ICA model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks.  hc-ICA can provide a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. To download the Matlab GUI-based hc-ICA Package,  please click here

DensParcorr: Dens-based Partial Correlation Estimation method

  Wang Y, Kang J, Kemmer PB and Guo Y (2016) An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation. Frontier in Neuroscience, 10:123. doi: 10.3389/fnins.2016.00123. 

 This paper presents an efficient and reliable statistical methods for estimating partial correlation matrices  for investigating direct functional connectvity in large-scale brain networks. To download the R code,  please click here 

TC-GICA: Temporal Concatenation group ICA models

     Guo Y (2011). A general probabilistic model for group independent component analysis (ICA) and its estimation methods. Biometrics.  67(4): 1532-1542.

 This paper proposes a  general temporal concatenation group ICA framework that can accomodate different  types of between-subject variability in temporal responses. To download the Matlab code,  please click here.  

Nonparametric estimator for lin's CCC for survival data

Guo Y and Manatunga AK (2007). Nonparametric estimation of the concordance correlation coefficient under univariate censoring. Biometrics, 63(1): 164-172.

This paper proposes a nonparametric estimation method for Lin's (1989, Biometrics 45, 255-268) concordance correlation coefficient (CCC) in the presence of censored observations such as those in survival studies.
To download the R code, please click here.