Python functions for simple nonparametric tests (both one-sample and paired).
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Updated
Nov 12, 2021 - Jupyter Notebook
Python functions for simple nonparametric tests (both one-sample and paired).
The code interface is written in R, and for the sake of speed, most parts are written in C++. However, no prerequisite knowledge for both languages is required to run the code. An R file called runInfHMM.R sources all needed functions to compile and run the code.
Investigate the impact of the fall of Berlin wall and COVID-19 global lockdown on CO2 concentration.
Nonparametric functional data analysis
Data and modeling notes for Nonparametric Statistics
The order-md algorithm is an adjustment of the order-m algorithm for estimating efficiency scores of decision making units (firms)
R codes for Nonparametric Statistics
Elements of Survival Analysis in R and Python
A comparion between parametric and nonparametric statistics using Python
UW course projects
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.
A collection of various non-parametric tests.
Code and documentation from density UQ review paper.
Implementation of the Polya completion algorithm for uncertainty restoration of the marginal MDP model
Tool for generating random variables that follows given frequency distibution using linear regressions in given intervals.
R Package providing functions to calculate pseudo-ranks and pseudo-rank based nonparametric test statistics.
Code and data of the paper "A nonparametric test of independence between two random variables of any kind"
Covariate adjustment methods using generalized empirical likelihood and robust estimators
Samples of my work in programming and data analysis.
The project involves sampling designs and summarizing data, maximum likelihood estimation of parameters, bootstrap, parametric and non-parametric inference, the analysis of variance, linear least squares, categorical data, elements of the decision theory and bayesian inference.
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