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- Earo Wang
Dr Earo Wang
Associate Investigator
University of Auckland
I’m a lecturer in the Department of Statistics at the University of Auckland. I earned my Ph.D. from Monash University, supervised by Professor Di Cook and Professor Rob J Hyndman. I was awarded the John Chambers Software Award for the R package {tsibble} in 2019. My research areas invovle statistical computing and graphics, and time series analysis.
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Research Interests:
data visualisation
Statistical computing
Time Series Analysis
Qualifications:
PhD
Prizes, awards and special recognition
2018
ASA Statistical Graphics Student Paper Award was awarded to Earo Wang. Awarded from the American Statistical Association.
2018
This year we had a great competition with 25 submissions. The committee selected four winners and one honorable mention. Thank you to our judges for all their hard work in reading and evaluating these papers: Heike Hofmann, Daniel Sussman, Raymond Wong, and Hao Helen Zhang (chair). The four winners are:
"BRISC: Bootstrap for rapid inference on spatial covariances", by Arkajyoti Saha (Department of Biostatistics, Johns Hopkins University)
"MM algorithms for variance component models", by Liuyi Hu (Department of Statistics, North Carolina State University).
"An asympirical smoothing parameters selection approach for SS-ANOVA models in large samples", by Xiaoxiao Sun (Department of Statistics, University of Georgia)
"Calendar-based graphics for visualizing people's daily schedules", by Earo Wang (Department of Econometrics and Business Statistics, Monash University)
The honorable mention is
"Dependency diagnostic: visually understanding pairwise variable relations", by Kevin Lin (Department of Statistics, Carnegie Mellon University)
Business Analytics Prize was awarded to Earo Wang. Awarded from the ACEMS (Monash University).
useR! 2018 Data Challenge (First Prize) was awarded to Earo Wang, Stephanie Kobakian. Awarded from the Altas of Living Australia & useR! 2018.
Impact and Engagement Award was awarded to Sevvandi Kandanaarachchi, Dianne Cook, Priyanga Dilini Talagala, Earo Wang, Lewis Mitchell, Nick Tierney, Rob Hyndman, Thiyanga Talagala. Awarded from the ACEMS.
Two other awardees:
Nick Spierson
Stuart Lee
Publications
Journal Articles
Publicly available softwares
Cook, D., Ebert A., Forbes J., Hofmann H., Hyndman R. J., Lumley T., et al.
(2019). eechidna: Exploring Election and Census Highly Informative Data Nationally for Australia.
Hyndman, R. J., Wang E., O'Hara-Wild M., Cook D., & Caceres G.
(2019). fable: Forecasting Models for Tidy Time Series.
Hyndman, R. J., Athanasopoulos G., Bergmeir C., Carceres G., Chhay L., O'Hara-Wild M., et al.
(2019). forecast: Forecasting Functions for Time Series and Linear Models.
O'Hara-Wild, M., Hyndman R. J., Wang E., Cook D., Talagala T., & Chhay L.
(2019). feasts: Feature Extraction And Statistics for Time Series.
Hyndman, R. J., & Wang E.
(2019). fabletools: Core Tools for Packages in the 'fable' Framework.
O'Hara-Wild, M., Hyndman R. J., Wang E., Cook D., & Athanasopoulos G.
(2019). fabletools: Core Tools for Packages in the 'fable' Framework.
Hyndman, R. J., O'Hara-Wild M., & Wang E.
(2019). tsibbledata: Diverse Datasets for 'tsibble'.
O'Hara-Wild, M., Hyndman R. J., Wang E., & Caceres G.
(2019). fable: Forecasting Models for Tidy Time Series.
Hyndman, R. J., Wang E., & Cook D.
(2019). feasts: Feature Extraction And Statistics for Time Series.
Hyndman, R. J., Lee A., Wang E., & Wickramasuriya S. L.
(2018). hts: Hierarchical and Grouped Time Series.
Wang, E., Cook D., Hyndman R. J., & O'Hara-Wild M.
(2018). tsibble: Tidy Temporal Data Frames and Tools.
Wang, E., Cook D., & Hyndman R. J.
(2018). sugrrants: Supporting Graphs for Analysing Time Series.
Hyndman, R., Wang E., Kang Y., Talagala T., Yang Y., & Ben Taieb S.
(2018). tsfeatures: Time Series Feature Extraction.
Wang, E., Cook D., & Hyndman R. J.
(2017). sugrrants: Provides ‘ggplot2’ graphics for analysing time series data..
Cook, D., Ebert A., Hofmann H., Hyndman R., Lumley T., Marwick B., et al.
(2017). eechidna: Exploring Election and Census Highly Informative Data Nationally for Australia.
