HR job title
- Associate Professor
- Contact Info
- 1 979 845 3141
Wong, Raymond Ka Wai Associate Professor
Positions
- Associate Professor, Statistics, College of Arts and Sciences
I work on statistical problems with modern data complications such as big data, high dimensionality and manifold structures.
- Overview
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- Research
- Teaching
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Overview
Publications
selected publications
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academic article
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Wong, R., Zubizarreta, J. R., Stuart, E. A., Small, D. S., & Rosenbaum, P. R.
(2024).
Handbook of Matching and Weighting Adjustments for Causal Inference. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
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Yi, S., Wong, R., & Gaynanova, I.
(2023).
Hierarchical nuclear norm penalization for multi-view data integration. Biometrics.
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Adak, A., Kang, M., Anderson, S. L., Murray, S. C., Jarquin, D., Wong, R., & Katzfu, M.
(2023).
Phenomic data-driven biological prediction of maize through field-based high throughput phenotyping integration with genomic data. erad216.
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Xue, W. u., Zhang, X., Chan, K., & Wong, R.
(2023).
RKHS-based covariate balancing for survival causal effect estimation. 1-25.
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Chen, S., He, K., He, S., Ni, Y., & Wong, R.
(2023).
Bayesian Nonlinear Tensor Regression with Functional Fused Elastic Net Prior. Technometrics.
ahead-of-print(ahead-of-print), 1-13.
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Wang, J., Wong, R., & Zhang, X.
(2022).
Low-Rank Covariance Function Estimation for Multidimensional Functional Data. 117(538), 809-822.
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Meskhi, M. M., Wolfe, N. E., Dai, Z., Frohlich, C., Miller, J. M., Wong, R., & Vilalta, R.
(2022).
A New Constraint on the Nuclear Equation of State from Statistical Distributions of Compact Remnants of Supernovae. Astrophysical Journal Letters.
932(1), l3-L3.
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Miao, R., Zhang, X., & Wong, R.
(2022).
A Wavelet-Based Independence Test for Functional Data With an Application to MEG Functional Connectivity. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
1-14.
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Wang, J., Wong, R., Yang, S., & Chan, K.
(2022).
Estimation of partially conditional average treatment effect by double kernel-covariate balancing. Electronic Journal of Statistics.
16(2), 4332-4378.
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Mao, X., Wong, R., & Chen, S. X.
(2021).
MATRIX COMPLETION UNDER LOW-RANK MISSING MECHANISM. 31(4), 2005-2030.
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Wang, J., Wong, R., Jun, M., Schumacher, C., Saravanan, R., & Sun, C.
(2021).
Statistical and Machine Learning Methods Applied to the Prediction of Different Tropical Rainfall Types. Environmental Research Communications.
3(11), 111001-111001.
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Li, J., Nguyen, T. V., Hegde, C., & Wong, R.
(2021).
Implicit Sparse Regularization: The Impact of Depth and Early Stopping
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Nguyen, T. V., Wong, R., & Hegde, C.
(2021).
Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis. 67(7), 4669-4692.
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Wang, J., Wong, R., Mao, X., & Chan, K.
(2021).
Matrix Completion with Model-free Weighting
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Wang, J., Wong, R., Yang, S., & Chan, K.
(2021).
Estimation of Partially Conditional Average Treatment Effect by Hybrid Kernel-covariate Balancing
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Zhou, Y. a., Wong, R., & He, K.
(2021).
Tensor Linear Regression: Degeneracy and Solution. 9, 7775-7788.
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Mao, X., Dutta, S., Wong, R., & Nettleton, D.
(2020).
Adjusting for Spatial Effects in Genomic Prediction. Journal of Agricultural, Biological, and Environmental Statistics.
25(4), 699-718.
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Zhou, Y. a., Wong, R., & He, K.
(2020).
CP Degeneracy in Tensor Regression
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Miao, R., Zhang, X., & Wong, R.
(2020).
A Wavelet-Based Independence Test for Functional Data with an Application to MEG Functional Connectivity
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Song, I., Cho, I. H., & Wong, R.
(2020).
An Advanced Statistical Approach to Data-Driven Earthquake Engineering. Journal of Earthquake Engineering.
24(8), 1245-1269.
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Zhou, Y. a., Wong, R., & He, K.
(2020).
Broadcasted Nonparametric Tensor Regression
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Su, Y. i., Wong, R., & Lee, T.
(2020).
Network Estimation via Graphon With Node Features. 7(3), 2078-2089.
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Liu, W., Mao, X., & Wong, R.
(2020).
Median Matrix Completion: from Embarrassment to Optimality
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Nguyen, T. V., Wong, R., & Hegde, C.
(2019).
Provably Accurate Double-Sparse Coding. JOURNAL OF MACHINE LEARNING RESEARCH.
20.
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Wong, R., Li, Y., & Zhu, Z.
(2019).
Partially Linear Functional Additive Models for Multivariate Functional Data. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
114(525), 406-418.
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Wang, J., Wong, R., & Lee, T.
(2019).
Locally linear embedding with additive noise. Pattern Recognition Letters.
123, 47-52.
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Wong, R., & Zhang, X.
(2019).
Nonparametric operator-regularized covariance function estimation for functional data. Computational Statistics and Data Analysis.
131, 131-144.
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Nguyen, T. V., Wong, R., & Hegde, C.
(2018).
Autoencoders Learn Generative Linear Models
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Wong, R., & Chan, K.
(2018).
Kernel-based covariate functional balancing for observational studies. Biometrika.
105(1), 199-213.
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Wong, R., Storlie, C. B., & Lee, T.
(2017).
A frequentist approach to computer model calibration. Journal of the Royal Statistical Society Series B: Statistical Methodology.
79(2), 635-648.
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Wong, R., & Lee, T.
(2017).
Matrix Completion with Noisy Entries and Outliers. JOURNAL OF MACHINE LEARNING RESEARCH.
18.
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Wong, R., Lee, T., Paul, D., & Peng, J.
(2016).
FIBER DIRECTION ESTIMATION, SMOOTHING AND TRACKING IN DIFFUSION MRI. Annals of Applied Statistics.
10(3), 1137-1156.
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Wong, R., Lee, T., Paul, D., & Peng, J.
(2016).
REJOINDER: "FIBER DIRECTION ESTIMATION, SMOOTHING AND TRACKING IN DIFFUSION MRI". Annals of Applied Statistics.
10(3), 1166-1169.
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Wong, R., Kashyap, V. L., Lee, T., & van Dyk, D. A.
(2016).
DETECTING ABRUPT CHANGES IN THE SPECTRA OF HIGH-ENERGY ASTROPHYSICAL SOURCES. Annals of Applied Statistics.
10(2), 1107-1134.
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Wong, R., Baines, P., Aue, A., Lee, T., & Kashyap, V. L.
(2014).
AUTOMATIC ESTIMATION OF FLUX DISTRIBUTIONS OF ASTROPHYSICAL SOURCE POPULATIONS. Annals of Applied Statistics.
8(3), 1690-1712.
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Han, S., Wong, R., Lee, T., Shen, L., Li, S., & Fan, X.
(2014).
A full bayesian approach for boolean genetic network inference. PLoS ONE.
9(12), e115806-e115806.
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Wong, R., Yao, F., & Lee, T.
(2014).
Robust Estimation for Generalized Additive Models. Journal of Computational And Graphical Statistics (JCGS).
23(1), 270-289.
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Lai, R., Lee, T., Wong, R., & Yao, F.
(2010).
Nonparametric Cepstrum Estimation via Optimal Risk Smoothing. IEEE Transactions on Signal Processing.
58(3), 1507-1514.
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Wong, R., Lai, R., & Lee, T.
(2010).
Structural break estimation of noisy sinusoidal signals. Signal Processing.
90(1), 303-312.
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Wong, R., Zubizarreta, J. R., Stuart, E. A., Small, D. S., & Rosenbaum, P. R.
(2024).
Handbook of Matching and Weighting Adjustments for Causal Inference. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
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conference paper
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Wei, Z., Wong, R., & Lee, T.
(2022).
EXTENDING THE USE OF MDL FOR HIGH-DIMENSIONAL PROBLEMS: VARIABLE SELECTION, ROBUST FITTING, AND ADDITIVE MODELING. Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing / sponsored by the Institute of Electrical and Electronics Engineers Signal Processing Society. ICASSP (Conference).
5707-5711.
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Woodruff, R., Beebe, N., Josan, P. K., Dutta, P., Short, A., Wong, R., ... Diaz-Artiles, A.
(2021).
3D Interactive Model of HERA to support ECLSS anomaly resolution using a Virtual Assistant. IEEE Aerospace Conference Proceedings.
1-10.
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Josan, P. K., Dutta, P., Woodruff, R., Beebe, N., York, K., Balcells-Quintana, O., ... Diaz-Artiles, A.
(2021).
Experimental Design & Pilot Testing for ECLSS Anomaly solution using Daphne-AT Virtual Assistant. IEEE Aerospace Conference Proceedings.
1-13.
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Wang, J., Wong, R., Mao, X., & Chan, K.
(2021).
Matrix Completion with Model-free Weighting
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Li, J., Nguyen, T. V., Hegde, C., & Wong, R.
(2021).
Implicit Sparse Regularization: The Impact of Depth and Early Stopping. Advances in Neural Information Processing Systems.
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Thanh, V. N., Wong, R., & Hegde, C.
(2019).
On the Dynamics of Gradient Descent for Autoencoders
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Nguyen, T. V., Wong, R., & Hegde, C.
(2018).
A Provable Approach for Double-Sparse Coding. Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence.
3852-3859.
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Liao, Z., Amariucai, G. T., Wong, R., & Guan, Y.
(2017).
The Impact of Discharge Inversion Effect on Learning SRAM Power-Up Statistics. 31-36.
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Wei, Z., Wong, R., & Lee, T.
(2022).
EXTENDING THE USE OF MDL FOR HIGH-DIMENSIONAL PROBLEMS: VARIABLE SELECTION, ROBUST FITTING, AND ADDITIVE MODELING. Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing / sponsored by the Institute of Electrical and Electronics Engineers Signal Processing Society. ICASSP (Conference).
5707-5711.
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institutional repository document
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Wang, J., Qi, Z., & Wong, R.
(2024).
A Fine-grained Analysis of Fitted Q-evaluation: Beyond Parametric Models
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Dong, J., Wong, R., & Chan, K.
(2024).
Balancing Method for Non-monotone Missing Data
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Hong, S., Qi, Z., & Wong, R.
(2024).
Distributional Off-policy Evaluation with Bellman Residual Minimization
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Roy, S., Wong, R., & Ni, Y.
(2023).
Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
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You, H., Wang, J., Wong, R., Schumacher, C., Saravanan, R., & Jun, M.
(2023).
Prediction of Tropical Pacific Rain Rates with Over-parameterized Neural Networks
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Wang, J., Wong, R., Zhang, X., & Chan, K.
(2023).
Flexible Functional Treatment Effect Estimation
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Chen, S., He, K., He, S., Ni, Y., & Wong, R.
(2023).
Bayesian Nonlinear Tensor Regression with Functional Fused Elastic Net Prior
-
Li, J., Nguyen, T. V., Hegde, C., & Wong, R.
(2023).
Implicit Regularization for Group Sparsity
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Yi, S., Wong, R., & Gaynanova, I.
(2022).
Hierarchical nuclear norm penalization for multi-view data
-
Wei, Z., Wong, R., & Lee, T.
(2022).
Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling
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Meskhi, M. M., Wolfe, N. E., Dai, Z., Frohlich, C., Miller, J. M., Wong, R., & Vilalta, R.
(2021).
A New Constraint on the Nuclear Equation of State from Statistical Distributions of Compact Remnants of Supernovae
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Wang, J., Qi, Z., & Wong, R.
(2021).
Projected State-action Balancing Weights for Offline Reinforcement Learning
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Wang, J., Qi, Z., & Wong, R.
(2024).
A Fine-grained Analysis of Fitted Q-evaluation: Beyond Parametric Models
Research
principal investigator on
co-principal investigator on
Teaching
teaching activities
- STAT211 Prin Of Statistics I Instructor
- STAT404 Statistical Computing Instructor
- STAT408 Intro To Linear Models Instructor
- STAT612 Thry Of Linear Models Instructor
- STAT616 Stat Aspects Of Mach Learn I Instructor
- STAT636 Applied Multivariate Anly Instructor
- STAT639 Data Mining & Analysis Instructor
- STAT685 Directed Studies Instructor
- STAT691 Research Instructor
Background
education and training
- Ph.D. in Statistics, University of California, Davis - (Davis, California, United States) 2014
- M.Phil. in Statistics, Chinese University of Hong Kong - (Shatin, Hong Kong, China) 2010
- B.S. in Statistics, minor in Mathematics and Risk Management Science, Chinese University of Hong Kong - (Shatin, Hong Kong, China) 2008
Contact
first name
- Raymond Ka Wai
has last name
- Wong
mailing address
-
Texas A&M University
Department Of Statistics
3143 TAMU
College Station, TX 77843-3143
USA
Other
- raywong@tamu.edu