About me

Matteo Bottai, Sc.D., is Professor of Biostatistics, Head of the Division of Biostatistics, and the Director of the Biostatistics Core Facility at Karolinska Institutet, He is also a Guest Professor at the Division of Mathematical Statistics at Stockholm University and Adjunct Professor at the Division of Biostatistics at the University of South Carolina. He received his doctoral degree from Harvard University, USA.

He has coauthored numerous papers in theoretical statistics, (e.g. Annals of Statistics, Bernoulli, Biometrika, Journal of the American Statistical Association), statistical methods (e.g. Biometrics, Biostatistics, Statistics in Medicine, Statistical Methods in Medical Research), computational statistics (e.g. Journal of Statistical Computation and Simulation, Stata Journal), epidemiology (e.g. American Journal of Epidemiology, Epidemiology, European Respiratory Journal), and medicine (e.g. British Medical Journal, Heart, Environmental Health Perspectives). He has served as Editor-in-Chief of Open Statistics in other capacities for several other statistical and medical journals (e.g. American Journal of Epidemiology, Biometrics, Journal of the American Statistical Association, Journal of the Royal Statistical Society B, Journal of Multivariate Analysis).

His current research focuses on statistical methods for inference on quantiles (e.g. parametric quantile process models, linear quantile mixed models, logistic quantile regression) and risk modeling. He served as Head of the Division of Biostatistics and member of the Faculty Senate at the University of South Carolina, and President of the South Carolina Chapter of the American Statistical Association. He was a recipient of the Fulbright Scholarship and of three Visiting Professor Awards. He is an elected member of the Delta Omega Honorary Society in Public Health.


Selected Articles

Rotnitzky A, Cox DR, Bottai M, Robins JM. Likelihood-based asymptotic inference with singular information. Bernoulli, 6(2): 243-284, 2000

Bottai M. Confidence regions when the Fisher information is zero. Biometrika, 90(1): 73-84, 2003

Bottai M, Orsini N. Confidence intervals for the variance component of random-effects linear models. The Stata Journal, 4(4): 429-435, 2004

Geraci M, Bottai M. Use of auxiliary data in semiparametric regression with nonignorable missing responses. Statistical Modeling, 6(4): 321-336, 2006

Geraci M, Bottai M. Quantile regression for longitudinal data using the asymmetric Laplace distribution. Biostatistics, 8(1): 140-54, 2007

Bottai M, Geraci M, Lawson A. Testing for Unusual Aggregation of Health Risk in Semiparametric Models. Statistics in Medicine, 27(15): 2902-2921, 2008

Liu Y, Bottai M. Mixed-effects models for conditional quantiles with longitudinal data. International Journal of Biostatistics, Vol. 5, Issue 1, Article 28, 2009

Bottai M. Quantile Regression, Encyclopedic Companion to Medical Statistics, 2nd edition, Everitt, B. and Palmer, C.(eds), Wiley & Sons, 2009

Bottai M, Cai B, McKeown ER. Logistic quantile regression for bounded outcomes. Statistics in Medicine, 29: 309-317, 2010

Bottai M, Zhang J. Laplace regression with censored data. Biometrical Journal, 52(4): 487-503, 2010

Bottai M. A regression method for modelling geometric rates. Statistical Methods in Medical Research, 26(6): 2700-2707, 2017 (first published 2015)

Bottai M, Orsini N, Geraci M. A gradient search maximization algorithm for the asymmetric laplace likelihood. Journal of Statistical Computation and Simulation. 85(10):1919-1925, 2015

Frumento P, Bottai M. Parametric modeling of quantile regression coefficient functions. Biometrics, 72(1): 74-84, 2016

Frumento P, Bottai M. Parametric modeling of quantile regression coefficient functions with censored and truncated data. Biometrics, 73: 1179-1188, 2017

Bossoli D, Bottai M. Marginal quantile regression for dependent data with a working odds-ratio matrix. Biostatistics, 19(4):529-545, 2018

Santacatterina M, Bottai M. Optimal probability weights for inference with constrained precision. Journal of the American Statistical Association, 113:523, 983-991, 2018

García‐Pareja C, Bottai M. On mean decomposition for summarizing conditional distributions. Stat, 2018:7:e208, 2018

Santacatterina M, García-Pareja C, Bellocco R, Sönnerborg A, Ekström AM, Bottai M. Optimal probability weights for estimating causal effects of time-varying treatments with marginal structural Cox models. Statistics in Medicine, 38:1891–1902, 2019

Bottai M, Cilluffo G. Nonlinear parametric quantile models. Statistical Methods in Medical Research, 29(12): 3757-3769, 2020

Frumento P, Bottai M, Fernández-Val I. Parametric Modeling of Quantile Regression Coefficient Functions With Longitudinal Data. Journal of the American Statistical Association, 116:534, 783-797, 2021

Bottai M, Discacciati A, Santoni G. Modeling the probability of occurrence of events. Statistical Methods in Medical Research, 30(8):1976-1987, 2021

Ekvall KO, Bottai M. Confidence regions near singular information and boundary points with applications to mixed models. Annals of Statistics, 50(3): 1806-1832, 2022

Bottai M, Kim T, Lieberman B, Luta G, Pena E. On Optimal Correlation-Based Prediction. The American Statistician, 76(4): 313-321, 2022

Columbu S, Frumento P, Bottai M. Modeling sign concordance of quantile regression residuals with multiple outcomes. International Journal of Biostatistics. 2022, doi: 10.1515/ijb-2022-0020

Ekvall KO, Bottai M. Concave likelihood-based regression with finite-support response variables. Biometrics 2022, doi: 10.1111/biom.13760

Bottai M. Estimating the risk of events with stprisk. Stata Journal, 22(4): 969–974, 2022



Doctor of Science, Harvard University