Button to scroll to the top of the page.

Updates

Campus health and safety are our top priorities. Get the latest from UT on COVID-19.

Get help with Zoom and more.

Linero, Antonio (Tony)
No

Antonio (Tony) Linero

Assistant Professor
Department of Statistics and Data Sciences



antonio.linero@austin.utexas.edu

Phone: 850-591-7359

Office Location
GDC 7.424

Postal Address
2317 SPEEDWAY
AUSTIN, TX 78712

Antonio (Tony) Linero joined The University of Texas at Austin faculty in 2019. Before joining UT Austin, he was an Assistant Professor in the Department of Statistics at Florida State University. He is a member of the International Society for Bayesian Analysis and the American Statistical Association. He serves as associate editor for Biometrics. In 2015, Dr. Linero received the Laplace award from the Section on Bayesian Statistical Science of the American Statistical Association. His research has been supported by the National Science Foundation and through work with the Science of Test Research Consortium. His research focuses on developing flexible and appropriate Bayesian methods for complex longitudinal data, as well as developing model selection and variable selection tools within the Bayesian nonparametric framework for high dimensional problems. His work also pursues Bayesian nonparametrics for missing data and causal inference problems in which analysts are forced to (i) confront the curse of dimensionality when estimating low-dimensional effects of interest, and (ii) are required to make in-principle unverifiable assumptions. 

PhD Statistics, University of Florida, 2015.

My research is broadly focused on developing flexible Bayesian methods. My work has focused on developing appropriate Bayesian methods for complex longitudinal data, as well as developing model selection and variable selection tools within the Bayesian nonparametric framework for high dimensional problems. I am also pursuing research in Bayesian nonparametrics for missing data and causal inference problems in which analysts are forced to (i) confront the curse of dimensionality when estimating low-dimensional effects of interest and (ii) are required to make in-principle unverifiable assumptions. Developing flexible Bayesian nonparametric models which give robust inferences in these scenarios is fraught with subtle challenges.