Recent Talks
When Likelihood Goes Wrong UQAM and CRM, April 2025 When Likelihood Goes Wrong UCLA Biostatistics Seminar, November 2024 When Likelihood Goes Wrong Pfizer Colloquium, U Conn, October 2024 Models and Parameters Joint Statistical Meetings, Portland OR, August 2024 Theoretical Statistics in Practice Women in Math Colloquium, U Ottawa, March 8 2024 Theoretical Statistics in Practice Professor T.D. Dwivedi Memorial Lecture, Concordia University, Montreal October 2023 The Importance of Foundations David R Cox Foundations of Statistics Lecture, Joint Statistical Meetings, Toronto ON August 2023 Statistical Theory and Practice 10th International Purdue Symposium on Statistics, Purdue University, West Lafayette IN June 2023 Partial Likelihood A Celebration of 50 Years of the Cox model in memory of Sir David Cox, London School of Hygiene and Tropical Medicine, UK November 2022 Likelihood and its Discontents COPSS Distinguished Achievement Award Lecture, Joint Statistical Meetings, Washington DC August 2022 Structural Inference and Asymptotics: in memory of Don Fraser , Joint Statistical Meetings, Washington DC August 2022 Remembering Sir David Cox , Joint Statistical Meetings, Washington DC August 2022 Fisher's Contributions to Mathematical Statistics , Caius College, Cambridge, April 2022 Data-dependent Priors , Workshop on perspectives in statistical modeling and inference, in honor of Ed George, Wharton, December 2021Likelihood inference in high dimensions
Replicability and Reproducibility: the interplay between statistical science and data science. Universita Roma, March 2021Three Rs: Reliability, Reproducibility, Replicability Hollander Lecture, Florida State University, October 2020Distributions for parameters
On collaboration CANSSI-Quebec Launch, Concordia University, November 2019In praise of small data
Gibbs Lecture , JMM 2020, January 2020 Rustagi Lecture , Ohio State University, Columbus, April 2019 Mathematics Colloquium , Imperial College, London, March 2019 Distinguished Lecture , College of Science, Rochester Institute of Technology, Rochester, February 2019
Asymptotic theory and practice Workshop on Data, Models and Inference in honour of Anthony Davison, Lausanne, September 2018 Directional testing and F -tests Workshop on Higher Order Asymptotics and Post-Selection Inference, Washington University of St. Louis, September 2018 Statistical science and data science Royal Society New Fellows Meeting, London, July 2018 Statistical Visualization Celebrating the Foundations and Impact of Statistics: A Symposium Honoring the 95th birthday of Herman Chernoff, Cambridge, April 2018 Approximate likelihood functions. BayesComp 2018, Universitat Pompeu Fabra, Barcelona, March 2018BFF Four: Are we converging? BFF 4 workshop , Harvard, May 2017
Distributions for parameters
Accurate directional inference for vector parameters.
Statistical Inference, Learning and Models for Big Data
World Statistics Congress , Marrakech, July 2017
Queens University , Kingston, March 2017
Fisher Memorial Lecture , London, October 2016
Video recording
UC San Diego , December 2015
Brock University , Fields-Brock Transdisciplinary Distinguished Lecture Series , November 2015, St. Catharines
University of Michigan , Department of Statistics, October 2015, Ann Arbor
Peking University , Center for Statistical Science, August 2015, Beijing
Penn State University , P.R. Krishnaiah Memorial Lecture, May 2015, State College
Approximate likelihoods
Some aspects of likelihood inference International Conference Ars Conjectandi, October 2013, Basel.
Principles of statistical inference joint with D.R. Cox, World Statistics Congress 2013, August 2013, Hong Kong
Directional tests for vector parameters Symposium in Honor of Jack Kalbfleisch, September 2012, Ann Arbor
Composite Likelihood -- Recent Progress
Likelihood inference in complex settings Presented at Foundations and Frontiers , Some thoughts on the theory of statistics / Pensées sur la théorie statistique
``Can chocolate save your life?" Public Lecture given at the
Institute for Mathematics and its Applications
Approximate inference for vector parameters
