About me


Matteo Bottai, Sc.D., is Professor and Head of the Unit of Biostatistics and the Director of the Biostatistics Core Facility at Karolinska Institutet. He received his doctoral degree from Harvard University, USA. He has coauthored numerous papers in theoretical statistics, (e.g. Bernoulli, Biometrika), applied statistics (e.g. Biostatistics, Statistical Modeling, Statistics in Medicine), computational statistics (e.g. Journal of Statistical Computation and Simulation, Stata Journal), epidemiology (e.g. Epidemiology, American Journal of Epidemiology, and European Respiratory Journal), and medicine (e.g. British Medical Journal, Heart, Environmental Health Perspectives). He has served as reviewer for several statistical and medical journals (e.g. Journal of the American Statistical Association, Journal of the Royal Statistical Society B, Journal of Multivariate Analysis, Biometrics, American Journal of Epidemiology, Journal of Internal Medicine). His current research focuses on statistical methods for inference on quantiles (e.g. parametric quantile process models, linear quantile mixed models, logistic quantile regression). He served as Head of the Division of Biostatistics at the University of South Carolina, 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 Adjunct Professor at the University of South Carolina and 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. Use of natural cubic splines for modeling pulmonary function in longitudinal epidemiological studies. Statistica, 61(1): 165-171, 2001

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)

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, March 25, 2021, doi: 10.1080/01621459.2021.1892702


Doctor of Science, Harvard University

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