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.

Diaz I, et al (2020) Non-parametric efficient causal mediation with intermediate confounders.

Diaz I, and Hejazi NS (2020) Causal mediation analysis for stochastic interventions.

Diaz I, and Williams N (2020) Non-parametric causal effects based on longitudinal modified treatment policies.

Hejazi NS et al (2020) Nonparametric causal mediation analysis for stochastic interventional (in) direct effects.

Rudolph KE, and Diaz I (2020) Efficiently transporting causal (in) direct effects to new populations under intermediate confounding and multiple mediators.

Xu Z, et al (2020). Subphenotyping depression using machine learning and electronic health records.

Yazdavar AH, et al (2020). Multimodal mental health analysis in social media.

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.

Statistical Software Using R Language

‘mhealth’: Preprocessing and visualizing mobile health data

‘mhealthFDA’: Functional data analysis for mobile health data

‘medshift’: Causal mediation analysis






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