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Novel Statistical Methods & Software

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

Zhang Y, et al (2021). Development and Validation of a Machine Learning Algorithm for Risk of Postpartum Depression among Pregnant Women. https://doi.org/10.1016/j.jad.2020.09.113

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

 

 

 

 

 

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