Novel Statistical Methods
Banerjee S and Monni S. (2020) An Orthogonally Equivariant Estimator of the Covariance Matrix in High Dimensions and Small Sample Size. https://arxiv.org/abs/1711.08411
Diaz I, et al (2020) Non-parametric efficient causal mediation with intermediate confounders. https://arxiv.org/abs/1912.09936.
Diaz I, and Hejazi NS (2020) Causal mediation analysis for stochastic interventions. https://doi.org/10.1111/rssb.12362
Diaz I, and Williams N (2020) Non-parametric causal effects based on longitudinal modified treatment policies. https://arxiv.org/abs/2006.01366
Hejazi NS et al (2020) Nonparametric causal mediation analysis for stochastic interventional (in) direct effects. https://arxiv.org/pdf/2009.06203.pdf
Rudolph KE, and Diaz I (2020) Efficiently transporting causal (in) direct effects to new populations under intermediate confounding and multiple mediators. https://arxiv.org/abs/2006.07708
Xu Z, et al (2020). Subphenotyping depression using machine learning and electronic health records. https://doi.org/10.1002/lrh2.10241
Yazdavar AH, et al (2020). Multimodal mental health analysis in social media. https://doi.org/10.1371/journal.pone.
DeFerio J, et al (2018). Using electronic health records to characterize prescription patterns: focus on antidepressants in nonpsychiatric outpatient settings. 10.1093/jamiaopen/ooy037
Statistical Software Using R Language
‘mhealth’: Preprocessing and visualizing mobile health data
https://github.com/jihuilee/mhealth
‘mhealthFDA’: Functional data analysis for mobile health data
https://github.com/jihuilee/mhealthFDA
‘medshift’: Causal mediation analysis
https://github.com/nhejazi/medshift
