Daniel Flores-Agreda

Daniel Flores-Agreda

Research Fellow (Biostatistics)

Monash University

Biography

I am a young statistician currently working as a Research Fellow in Biostatistics at the School of Public Health and Preventive Medicine of Monash University. I obtained my PhD in Statistics in 2017 at the University of Geneva, then worked as a Junior Lecturer at the Faculty of Business and Economics of the University of Lausanne teaching courses on Forecasting.

I conduct research in mixed models and their applications. My current projects involve bootstrapping models with random effects and applications of in the field of survival analysis.

Interests

  • Mixed-Effects Models
  • Bootstrapping Methods
  • Computational Statistics
  • Survival Analysis
  • Forecasting

Education

  • PhD in Statistics, 2017

    University of Geneva

  • MSc in Statistics, 2012

    University of Geneva

  • BSc in Economics, 2009

    University of Geneva

Publications and Preprints

A flexible tool for inference of Accelerated Failure Time models with clustered data

Working Paper
Daniel Flores-Agreda, Eva Cantoni, Rory Wolfe, Stephane Heritier
Code

Bootstrapping generalized linear mixed models via a weighted Laplace approximation

A general way of bootstrapping based on externally generated random weights and the Laplace Approximation for integrals - Under review
Daniel Flores-Agreda, Eva Cantoni
Code

Bootstrap estimation of uncertainty in prediction for generalized linear mixed models

A new way of computing the uncertainty in prediction of Random Effects with a novel Bootstrapping method.
Daniel Flores-Agreda, Eva Cantoni
PDF DOI Code

Talks

Recent & Upcoming

Bootstrapping clustered data via a weighted Laplace approximation

A discussion of Bootstrapping applied to Generalized Linear Mixed Models and Accelerated Failure Times.
Apr 8, 2020 9:30 AM — 10:30 AM School of Public Health and Preventive Medicine - Monash University
Daniel Flores-Agreda, Eva Cantoni, Rory Wolfe, Stephane Heritier
Slides

Posts

Examples of Implementation

Bootstrapping a Poisson GLMM with glmmBoot

This code to illustrates the implementation of the bootstrapping methods described in (Flores-Agreda and Cantoni, Under Review) in a real-data context.
Last updated on May 28, 2020 Bootstrap, GLMM, R

Experience

 
 
 
 
 

Research Fellow (Biostatistics)

School of Public Health and Preventive Medicine
Monash University

Feb 2019 – Present Melbourne, Australia
Post-Doctoral Researcher funded by the National Health and Medical Research Council (NHMRC) on a Grant entitled Enhancing aspects of time-to-event analysis methodology in randomised trials
 
 
 
 
 

Junior Lecturer in Statistics

Faculty of Business and Economics (HEC Lausanne)
University of Lausanne

Feb 2018 – Jul 2019 Lausanne, Switzerland

Instructor of two classes in the Business Analytics track of the MSc. in Management program.

  • Time Series and Forecasting (2018)
  • Forecasting I (2019)
 
 
 
 
 

Teaching and Research Assistant

Research Center for Statistics
Geneva School of Economics and Management
University of Geneva

Feb 2012 – Jul 2017 Geneva, Switzerland

Research in the context of a Doctoral Thesis project entitled On the Inference of Random Effects in Generalized Linear Mixed Models.

Teaching Assistant for various undergraduate and graduate-level courses.

BSc. in International Relations

  • Mathematics I

BSc. in Economics

  • Statistics I
  • Statistics II

MSc. in Economics

  • Microeconometrics

MSc. in Statistics

  • Generalized Linear and Additive Models
  • Experimental Design

Skills

R

Programming & Analysis

Programming

MATLAB, C++, Python

Data Analysis

SPSS, Stata, S-Plus

Operating Systems

Windows, macOS, Ubuntu Linux

Typesetting

MS Office, Apache OpenOffice, LaTeX, R Markdown

Languages

Fluent in Spanish, French, English

Contact