Ian McKeague, PhD
- Professor of Biostatistics
On the web
Overview
Professor McKeague was on the faculty of the Department of Statistics of the Florida State University, 1980-2004. He was on sabbatical leave at the Mathematical Sciences Research Institute of the University of California at Berkeley, and then at the Laboratoire de Modalisation et Calcul of the University Joseph Fourier, Grenoble, France, 1991-1992. He served as Chair of the FSU Statistics department, 1996-99, and was named the Ralph A. Bradley Professor of Statistics at FSU in 2000.
He has been a Professor of Biostatistics at Columbia University since 2004. During 2021-2022 he was on leave from Columbia at the City University of Hong Kong as (founding) Head and Chair Professor of the Department of Biostatistics.
Research interests include post-selection inference, empirical likelihood, wearable device data, order-restricted inference, non-standard asymptotics, statistical methods in physical oceanography, functional data analysis, inference for stochastic processes, survival analysis, competing risks models for HIV/AIDS data, Markov chain Monte Carlo and Bayesian methods, efficient estimation for semiparametric models, missing data, counting processes and spatial point processes.
He has been a Professor of Biostatistics at Columbia University since 2004. During 2021-2022 he was on leave from Columbia at the City University of Hong Kong as (founding) Head and Chair Professor of the Department of Biostatistics.
Research interests include post-selection inference, empirical likelihood, wearable device data, order-restricted inference, non-standard asymptotics, statistical methods in physical oceanography, functional data analysis, inference for stochastic processes, survival analysis, competing risks models for HIV/AIDS data, Markov chain Monte Carlo and Bayesian methods, efficient estimation for semiparametric models, missing data, counting processes and spatial point processes.
Academic Appointments
- Professor of Biostatistics
Credentials & Experience
Education & Training
- BA, 1975 University of Cambridge
- MA & MMath, 1976 University of Cambridge
- PhD, 1980 University of North Carolina at Chapel Hill
Committees, Societies, Councils
- Associate editor of the Annals of Statistics for 7 years
- Associate editor of the Journal of the American Statistical Association for 11 years
- Editorial Board, Journal of the American Statistical Association
- Editorial Board, Journal of Statistical Science
- Editorial Board, Journal of Statistical Inference for Stochastic Processes
- Editorial Board, the International Journal of Biostatistics
- 2020-2023, Co-Editor of the Journal of the American Statistical Association.
Honors & Awards
- Fellow of the Institute of Mathematical Statistics
- Fellow of the American Statistical Association
Research
Areas of expertise include post-selection inference, functional data analysis, empirical likelihood methods, survival analysis, competing risks models for HIV/AIDS data, inference for stochastic processes, and Markov chain Monte Carlo. He has also done significant work on simultaneous inference, efficient estimation for semi-parametric models, missing data models, Bayesian statistics, wavelet methods and martingale and counting process methods. Published over 100 peer-reviewed statistical methodology articles as lead author, including 16 articles in the Annals of Statistics, and 10 in the Journal of the American Statistical Association. He has been the doctoral advisor of 18 students (most of whom have positions in academia).
Research Interests
- Biostatistical Methods
- Global Health
- Survival Analysis
Grants
NSF Grant DMS-2112938, “Post-selection Inference for Survival Outcomes in Precision Medicine.” Co-Principal Investigator (joint with Min Qian), 2021–2024.
NIH Grant 1R01 AG062401, “Inferential Methods for Functional Data from Wearable Devices,” Project Director and Principal Investigator, 2019–2024.
Past Grants
NIH Grant 2R01 GM095722-05, “Post-selection Inference and Trajectory Analysis,” Project Director and Principal Investigator, 2015–2020.
NSF Grant DMS-1307838, “Optimal Treatment Policies and Adaptive Screening for Functional Predictors,” Project Director and Principal Investigator, 2013–2016.
NIH Grant R01 GM095722-01, “Point Impact and Sparsity in Functional Data Analysis,” Project Director and Princi- pal Investigator, 2011–2015.
NSF Grant DMS-0806088, “Sparse Predictors in Functional Data Analysis,” Project Director and Principal Investigator, 2008–2012.
NSF Grant DMS-0505201, “Hybrid Likelihood Methods,” Project Director and Principal Investigator, 2005–2009.
NSF Grant ATM-0222244, Opportunities for Research Collaborations between the Mathematical Sciences and the Geosciences Program, “Ocean Circulation Climatology and Dynamics Using Bayesian Hierarchical Methods,” Project Director and Principal Investigator, 2002–2006.
NSF Grant DMS-1307838, “Optimal Treatment Policies and Adaptive Screening for Functional Predictors,” Project Director and Principal Investigator, 2013–2016.
NIH Grant R01 GM095722-01, “Point Impact and Sparsity in Functional Data Analysis,” Project Director and Princi- pal Investigator, 2011–2015.
NSF Grant DMS-0806088, “Sparse Predictors in Functional Data Analysis,” Project Director and Principal Investigator, 2008–2012.
NSF Grant DMS-0505201, “Hybrid Likelihood Methods,” Project Director and Principal Investigator, 2005–2009.
NSF Grant ATM-0222244, Opportunities for Research Collaborations between the Mathematical Sciences and the Geosciences Program, “Ocean Circulation Climatology and Dynamics Using Bayesian Hierarchical Methods,” Project Director and Principal Investigator, 2002–2006.
Selected Publications
I. W. McKeague and B. Sen. Fractals with Point Impact in Functional Linear Regression. The Annals of Statistics 38 2559–2586 (2010).
Lopez-Pintado, S. & McKeague, I.W. (2013). Recovering gradients from sparsely observed functional data. Biometrics, 69(2): 396-404.
McKeague, I.W. and Qian, M. (2014). Estimation of treatment policies based on functional predictors. Statistica Sinica, 24(3): 1461-1485.
I.W. McKeague, A. S. Brown, Y. Bao, S. Hinkka-Yli-Saloma ki, J. Huttunen, Sourander, A. Autism with Intellectual Disability Related to Dynamics of Head Circumference Growth during Early Infancy. Biological Psychiatry 77 833–840 (2015).
McKeague I.W. and Qian M. (2018). Marginal screening of 2 x 2 tables in large-scale case-control studies. Biometrics, 75, 163-171.
H.-W.Chang and I.W. McKeague. Nonparametric Testing for Multiple Survival Functions with Non-Inferiority Margins. The Annals of Statistics 47 205–232 (2019).
McKeague I.W. and Qian M. (2015). An adaptive resampling test for detecting the presence of significant predictors. Journal of the American Statistical Association. 110(512), 1422-1433. T&M Special Invited Paper for 2015 (with discussants).
H.-W. Chang and I. W. McKeague. Empirical Likelihood-based Inference for Functional Means with Application to Wearable Device Data. Journal of the Royal Statistical Society – Series B, 84(5), 1947–1968 (2022).
I. W. McKeague and X. (Henry) Zhang. Significance Testing for Canonical Correlation Analysis in High Dimensions. Biometrika, 109(4), 1067–1083 (2022).
T.-J. Huang, A. Luedtke and I. W. McKeague. Efficient Estimation of the Maximal Association between Multiple Predictors and a Survival Outcome. The Annals of Statistics 51 1965–1988 (2023).
Lopez-Pintado, S. & McKeague, I.W. (2013). Recovering gradients from sparsely observed functional data. Biometrics, 69(2): 396-404.
McKeague, I.W. and Qian, M. (2014). Estimation of treatment policies based on functional predictors. Statistica Sinica, 24(3): 1461-1485.
I.W. McKeague, A. S. Brown, Y. Bao, S. Hinkka-Yli-Saloma ki, J. Huttunen, Sourander, A. Autism with Intellectual Disability Related to Dynamics of Head Circumference Growth during Early Infancy. Biological Psychiatry 77 833–840 (2015).
McKeague I.W. and Qian M. (2018). Marginal screening of 2 x 2 tables in large-scale case-control studies. Biometrics, 75, 163-171.
H.-W.Chang and I.W. McKeague. Nonparametric Testing for Multiple Survival Functions with Non-Inferiority Margins. The Annals of Statistics 47 205–232 (2019).
McKeague I.W. and Qian M. (2015). An adaptive resampling test for detecting the presence of significant predictors. Journal of the American Statistical Association. 110(512), 1422-1433. T&M Special Invited Paper for 2015 (with discussants).
H.-W. Chang and I. W. McKeague. Empirical Likelihood-based Inference for Functional Means with Application to Wearable Device Data. Journal of the Royal Statistical Society – Series B, 84(5), 1947–1968 (2022).
I. W. McKeague and X. (Henry) Zhang. Significance Testing for Canonical Correlation Analysis in High Dimensions. Biometrika, 109(4), 1067–1083 (2022).
T.-J. Huang, A. Luedtke and I. W. McKeague. Efficient Estimation of the Maximal Association between Multiple Predictors and a Survival Outcome. The Annals of Statistics 51 1965–1988 (2023).