En este repositorio se almacenan software y datos que permiten reproducir los resultados de investigación publicados por grupos de la Facultad de Ingeniería Química de la Universidad Nacional del Litoral.
Grupo de Estadística / Statistics Group
This repository contains code to reproduce results in:
C. Antunes Percíncula, L. Forzani and R. Toledano, “Invariant Moments of the Wishart Distribution: A Sage Package and Website Implementation” (submitted).
The main code for computing the invariant moments of a Wishart distribution is written in SAGE. There is also a webpage to compute them interactively: https://antunescarles.github.io/wishart-moments-calculator/
The paper explaining the methodology can be found HERE and the CODE HERE and to reproduce the results run the file HERE.
R. D. Cook, L. Forzani and D. Tomassi, “LDR: a package for likelihood-based sufficient dimension reduction”. Journal of Statistical Software, Vol. 39(3), 2011.
The code for the proposed method is written in MATLAB. LDR package (CODE).
Duarte, S., L. Forzani, R. Garcia Arancibia, P. Llop and D. Tomassi, “Socioeconomic Index for Income and Poverty Prediction: A Sufficient Dimension Reduction Approach”. The code for the PFCmix method is written in MATLAB. The code for PCA mix method is written in R. Instructions on how to use the code and reproduce results reported in the manuscript are detailed in the README.pdf file located in the main folder (CODE).
L. Forzani, R. Garcia Arancibia, P. Llop and D. Tomassi, “Supervised dimension reduction for ordinal predictors” (submitted).
The main code for the proposed method is written in MATLAB. Nevertheless, some comparisons with other methods require running scripts in R. Instructions on how to use the code and reproduce results reported in the manuscript are detailed in the README.pdf file located in the main folder.
Supervised dimension reduction for ordinal predictors [LINK CODE] [LINK ARXIV]
María José Llop, Andrea Gómez, Pamela Llop, María Soledad López, Gabriela V. Müller, “Prediction of leptospirosis outbreaks by hydroclimatic covariates: a comparative study of statistical models”. The code for the proposed method is written in R. Instructions on how to reproduce the results reported in the manuscript are detailed in the README.docx file located in the main folder (CODE).
D. Tomassi, L. Forzani, E. Bura and R. Pfeiffer, “Sufficient dimension reduction for censored predictors“. To appear in Biometrics.
The code for the proposed method is written in MATLAB. Usage instructions are detailed in the README.pdf file located in the main folder. Sufficient dimension reduction for censored predictors (CODE).