This is a project I did in the Spring of 2017 for the graduate course, Statistical Computing. In this project, I used a bootstrap approach to obtain a 90% confidence interval for an estimate of the coefficient variation
Permutational group-sequential tests for time-to-event data based on the log-rank test statistic.
Code for our paper: "Adaptive Geo-Topological Independence Criterion".
The purpose of this inferential analysis project is to determine the impact of regulations on handheld devices while driving.
Machine learning tools for the analysis of systems serology data
This is the code of a group university project on insurance premiums I took part in. Nonparametric statistics has been used for the data analysis and a shiny app has been implemented to show health insurance premium predictions. I thank Anna Iob, Martina Garavaglia and Veronica Mazzola who have partecipated in the project realisation.
In this project, we analyze differences in performance metrics for collegiate basketball teams that have qualified for March Madness versus those that have not using a variety of Monte Carlo Simulation methods in R.
This was an experiment of a 2-sample permutation or randomization T-test with a small pre-and post-survey. The client had a small sample (n<30) and the experiment informed the research results.
The aim of this project is to apply different permutation tests on different datasets alongside checking their accuracy and flaws. This project was part of my thesis under the supervision of Dr. Bahrak at the University of Tehran.
Topological Learning for Brain Networks (Annals of Applied Statistics; MICCAI 2021)
Nonparametric analysis of 2020 US presidential elections.
Permutation testing in single cell analysis
Easy-to-use collection of statistical methods and techniques, all written in R 🗂
Analysis code and Latex source of the manuscript describing the conditional permutation test of confounding bias in predictive modelling.
r code for permutation tests
Statistical package for fast exact tests and simulations
Permutation-Based FDR Point and Confidence Interval Estimation
Tools for analyzing and quantifying effects of confounder variables on machine learning model predictions.
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