Daniel J. McDonald
Daniel J. McDonald
Associate Professor of Statistics, University of British Columbia
Zweryfikowany adres z stat.ubc.ca - Strona główna
Cytowane przez
Cytowane przez
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ...
Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022
A primer on the mortgage market and mortgage finance
DJ McDonald, DL Thornton
Review-Federal Reserve Bank of Saint Louis 90 (1), 31, 2008
Does increased sexual frequency enhance happiness?
G Loewenstein, T Krishnamurti, J Kopsic, D Mcdonald
Journal of Economic Behavior & Organization 116, 206-218, 2015
The lasso, persistence, and cross-validation
D Homrighausen, D McDonald
International conference on machine learning, 1031-1039, 2013
The impact of price discounts and calorie messaging on beverage consumption: a multi-site field study
JJS Jue, MJ Press, D McDonald, KG Volpp, DA Asch, N Mitra, ...
Preventive medicine 55 (6), 629-633, 2012
An open repository of real-time COVID-19 indicators
A Reinhart, L Brooks, M Jahja, A Rumack, J Tang, S Agrawal, W Al Saeed, ...
Proceedings of the National Academy of Sciences 118 (51), e2111452118, 2021
Leave-one-out cross-validation is risk consistent for lasso
D Homrighausen, DJ McDonald
Machine learning 97 (1), 65-78, 2014
Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?
DJ McDonald, J Bien, A Green, AJ Hu, N DeFries, S Hyun, NL Oliveira, ...
Proceedings of the National Academy of Sciences 118 (51), e2111453118, 2021
Nonparametric risk bounds for time-series forecasting
DJ McDonald, CR Shalizi, M Schervish
Journal of Machine Learning Research 18 (32), 1-40, 2017
Estimating beta-mixing coefficients
D Mcdonald, C Shalizi, M Schervish
Proceedings of the Fourteenth International Conference on Artificial …, 2011
Risk-consistency of cross-validation with lasso-type procedures
D Homrighausen, DJ McDonald
Statistica Sinica 27 (3), 1017-1036, 2017
Estimating beta-mixing coefficients via histograms
DJ McDonald, CR Shalizi, M Schervish
Electronic Journal of Statistics 9 (2), 2855-2883, 2015
Rademacher Complexity of Stationary Sequences
DJ McDonald, CR Shalizi
arXiv preprint arXiv:1106.0730, 2017
On the Nyström and column-sampling methods for the approximate principal components analysis of large datasets
D Homrighausen, DJ McDonald
Journal of Computational and Graphical Statistics 25 (2), 344-362, 2016
Flexible analysis of TSS mapping data and detection of TSS shifts with TSRexploreR
RA Policastro, DJ McDonald, VP Brendel, GE Zentner
NAR Genomics and Bioinformatics 3 (2), lqab051, 2021
A study on tuning parameter selection for the high-dimensional lasso
D Homrighausen, DJ McDonald
Journal of Statistical Computation and Simulation 88 (15), 2865-2892, 2018
Minimax Density Estimation for Growing Dimension
DJ McDonald
AISTATS 54, 194-203, 2017
Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression
L Ding, DJ McDonald
Bioinformatics 33 (14), i350-i358, 2017
Generalization error bounds for stationary autoregressive models
DJ McDonald, CR Shalizi, M Schervish
arXiv preprint arXiv:1103.0942, 2011
Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing
DĐ Phạm, DJ McDonald, L Ding, MB Nebel, AF Mejia
NeuroImage 270, 119972, 2023
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