Om mig
Matteo Bottai är professor i biostatistik, chef för avdelningen för biostatistik och vetenskaplig chef för Biostatistics Core Facility vid Karolinska Institutet. Han är även gästprofessor vid avdelningen för matematisk statistik vid Stockholms universitet och adjungerad professor vid avdelningen för biostatistik vid University of South Carolina. Han tog sin doktorsexamen vid Harvard University i USA.
Läs mer om Matteo Bottai på hans engelska profilsida
Vetenskapliga artiklar i urval
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
Utbildning
Doctor of Science, Harvard University, USA.