Felipe Elorrieta López

 

FELIPE-ELORRIETA
Teléfono: +56 0227182030
Oficina: 418
Correo: felipe.elorrieta[at]usach.cl
Jerarquía: Asociado
Títulos y/o Grados: 
Licenciado en Estadística, Universidad de Santiago, 2010, Santiago, Chile.
Ingeniero Estadístico, Universidad de Santiago, 2011, Santiago, Chile.
Magister en Estadística, Pontificia Universidad Católica, 2013, Santiago, Chile.
Doctor en Estadística, Pontificia Universidad Católica, 2018, Santiago, Chile.
 

 

Lineas de Investigación: 

Series de Tiempo

Algoritmos de Machine Learning

Estadistica Espacial

 

Publicaciones Seleccionadas:

Varas S, Elorrieta F, Vargas C., Villalobos Dintrans P, Castillo C, Martinez Y, Ayala A, Maddaleno M. (2022) Factors associated with change in adherence to COVID-19 personal protection measures in the Metropolitan Region, Chile. PLOS ONE 17(5): e0267413. https://doi.org/10.1371/journal.pone.0267413

Ayala A, Villalobos Dintrans P, Elorrieta F, Castillo C, Vargas C, Maddaleno M. Identification of COVID-19 Waves: Considerations for Research and Policy. International Journal of Environmental Research and Public Health. 2021; 18(21):11058. https://doi.org/10.3390/ijerph182111058

Elorrieta, F., Eyheramendy, S., Palma, W., & Ojeda, C. (2021). A novel bivariate autoregressive model for predicting and forecasting irregularly observed time series. Monthly Notices of the Royal Astronomical Society, 505(1), 1105-1116. https://doi.org/10.1093/mnras/stab1216

Elorrieta, F., Eyheramendy, S. and Palma, W. 2019. Discrete-time autoregressive model for unequally spaced time-series observations. Astronomy & Astrophysics, 627(A120):1–12. URL https://doi.org/10.1051/0004-6361/201935560

Eyheramendy, S., Elorrieta, F., and Palma, W. 2018. An irregular discrete time series model to identify residuals with autocorrelation in astronomical light curves. Monthly Notices of the Royal Astronomical Society, 481(4):4311–4322. URL http://dx.doi.org/10.1093/mnras/sty2487

Elorrieta, F., Eyheramendy, S., Jordán, A., Dékány, I., Catelan, M. 2016. A Machine Learned Classifier for Rr Lyrae in The VVV Survey. Astronomy & Astrophysics, 595(A82):1-11. URL https://doi.org/10.1051/0004-6361/201628700

Link de Interés:

GEMVEP: http://gemvep.usach.cl/

MAS: https://www.astrofisicamas.cl/

ALeRCE: http://alerce.science/

Red-Datos: http://reddatos.usach.cl/

Mis investigaciones y repositorios:

Google Scholar: https://scholar.google.cl/citations?hl=es&user=iux_OB0AAAAJ

Github: https://github.com/felipeelorrieta/

LinkedIn: https://www.linkedin.com/in/felipe-elorrieta-0165a259/

ResearchGate: https://www.researchgate.net/profile/Felipe_Elorrieta_Lopez

ORCID: https://orcid.org/0000-0002-1835-7433

Mis paquetes estadísticos:

iAR Package (R): https://cran.r-project.org/web/packages/iAR/index.html

iAR Package (Python): https://pypi.org/project/iar/