Publications

  • Shen, X. and Huang, H.-C. (2019+). Discussion of Rosset’s and Tibshirani’s paper, entitled “From fixed-X to random-X regression: bias-variance decompositions, covariance penalties, and prediction error estimation, Journal of the American Statistical Association, to appear.
  • Huang, G., Chen, L.-J., Hwang, W.-H., Tzeng, S. and Huang, H.-C. (2019+). Real-time PM2.5 mapping and anomaly detection from AirBoxes in Taiwan, Environmetrics, to appear.
  • Tzeng, S. and Huang, H.-C. (2018). Resolution adaptive fixed rank kriging, Technometrics, 60, 198-208.
  • Wang, W.-T. and Huang, H.-C. (2018). Regularized spatial maximum covariance analysis. Environmetrics, 29, https://doi.org/10.1002/env.2481. (R Package on CRAN)
  • Wang, W.-T. and Huang, H.-C. (2017). Regularized principal component analysis for spatial data. Journal of Computational and Graphical Statistics, 26, 14-25. (R Package on CRAN)
  • Chang, C.-H., Huang, H.-C., Ing, C.-K. (2017). Mixed domain asymptotics for a stochastic process model with time trend and measurement error. Bernoulli Journal, 23, 159-190.
  • Huang, H.-C. and Lee, C.M. Thomas (2016). High-dimensional covariance estimation under the presence of outliers. Statistics and its Interface, 9, 461-468.
  • Tzeng, S. and Huang, H.-C. (2015). Non-stationary multivariate spatial covariance estimation via low-rank regularization. Statistica Sinica, 25, 151-172.
  • Chang, C.-H., Huang, H.-C., Ing, C.-K. (2014). Asymptotic theory of generalized information criterion for geostatistical regression model selection. The Annals of Statistics, 42, 2441-2468.
  • Chen, Y.-P. and Huang, H.-C. (2013). Nonstationary spatial modeling using penalized likelihood. Statistica Sinica, 23, 987-1017.
  • Shen, X., Huang, H.-C., and Pan, W. (2012). Simultaneous supervised clustering and feature selection over a graph. Biometrika, 99, 899-914. (R Package on CRAN)
  • Lai, R, Huang, H.-C., and Lee, T. (2012). Fixed and random effects selection in nonparametric additive mixed models. Electronic Journal of Statistics, 6, 810-842.
  • Chen, C.-S. and Huang, H.-C. (2012). Geostatistical model averaging based on conditional information criteria. Environmental and Ecological Statistics, 19, 23-35.
  • Hsu, N.-J., Chang, Y.-M., and Huang, H.-C. (2012). A Group Lasso Approach for Nonstationary Spatial-Temporal Covariance Estimation. Environmetrics, 23, 12-23.
  • Chen, C.-S. and Huang, H.-C. (2011). An improved Cp criterion for spline smoothing. Journal of Statistical Planning and Inference, 141, 445-452.
  • Huang, H.-C., Hsu, N.-J., Theobald, D., and Breidt, F. J. (2010). Spatial Lasso with applications to GIS model selection. Journal of Computational and Graphical Statistics, 19, 963-983.
  • Shen, X. and Huang, H.-C. (2010). Grouping pursuit through a regularization solution surface. Journal of the American Statistical Association, 490, 727-739.
  • Zhu, J., Huang, H.-C., and Reyes, P. E. (2010). On selection of spatial linear models for lattice data. Journal of the Royal Statistical Society, Series B, 72, 389-402 (supplemental materials).
  • Chen, Y.-P., Huang, H.-C., and Tu, I.-P. (2010). A new approach for selecting the number of factors. Computational Statistics and Data Analysis, 54, 2990-2998.
  • Chang, Y.-M., Hsu, N.-J., and Huang, H.-C. (2010). Semiparametric estimation of nonstationary spatial covariance function. Journal of Computational and Graphical Statistics, 19, 117-139.
  • Huang, H.-C. and Chen, C.-S. (2007). Optimal geostatistical model selection. Journal of the American Statistical Association, 102, 1009-1024.
  • Huang, H.-C., Martinez, F., Mateu, J. and Montes, F. (2007). Model comparison and selection for stationary space-time models.Computational Statistics and Data Analysis, 51, 4577-4596.
  • Johannesson, G., Cressie, N.,and Huang, H.-C. (2007). Dynamic multi-resolution spatial models. Environmental and Ecological Statistics, 14, 5-25.
  • Huang, H.-C. and Lee, T. (2006). Data adaptive median filters for signal and image denoising using a generalized SURE criterion. IEEE Signal Processing Letters, 13, 561-564.
  • Shen, X. and Huang, H.-C. (2006). Optimal model assessment, selection and combination. Journal of the American Statistical Association, 101, 554-568.
  • Tzeng, S., Huang, H.-C., and Cressie, N. (2005). A fast, optimal spatial-prediction method for massive datasets. Journal of the American Statistical Association, 100, 1343-1357.
  • Zhu, J., Huang, H.-C., and Wu, C.-T. (2005). Modeling spatial-temporal binary data using Markov random fields. Journal of Agricultural, Biological, and Environmental Statistics, 10, 212-225.
  • Shen, X., Huang, H.-C., and Ye, J. (2004). Inference after model selection. Journal of the American Statistical Association, 99, 751-762.
  • Shen, X., Huang, H.-C., and Ye, J. (2004). Comment on “The estimation of prediction error: covariance penalties and cross-validation” by B. Efron. Journal of the American Statistical Association, 99, 634-637.
  • Shen, X., Huang, H.-C., and Ye, J. (2004). Adaptive model selection and assessment for exponential family models. Technometrics, 46, 306-317.
  • Huang, H.-C. and Hsu, N.-J. (2004). Modeling transport effects on ground-level ozone using a non-stationary space-time model. Environmetrics, 15, 251-268.
  • Shen, X., Huang, H.-C., and Cressie, N. (2002). Nonparametric hypothesis testing for a spatial signal. Journal of the American Statistical Association, 97, 1122-1140 (R Package on CRAN).
  • Huang, H.-C., Cressie, N., and Gabrosek, J. (2002). Fast, resolution-consistent spatial prediction of global processes from satellite data. Journal of Computational and Graphical Statistics, 11, 63-88.
  • Huang, H.-C. and Cressie, N. (2001). Multiscale graphical modeling in space: Applications to command and control. In Spatial Statistics: Methodological Aspects and Applications (M. Moore ed.). Springer Lecture Notes in Statistics, 159, Springer-Verlag, New York, 83-113.
  • Huang, H.-C. and Cressie, N. (2000). Asymptotic properties of MLEs for partially ordered Markov models, Statistica Sinica, 10, 1325-1344.
  • Huang, H.-C. and Cressie, N. (2000). Deterministic/stochastic wavelet decomposition for recovery of signal from noisy data. Technometrics, 42, 262-276. (Matlab code is available from the Wavelet Denoising software written by Antoniadis et al., 2001)
  • Cressie, N. and Huang, H.-C. (1999). Classes of nonseparable spatio-temporal stationary covariance functions. Journal of the American Statistical Association, 94, 1330-1340.
  • Huang, H.-C. and Cressie, N. (1999). Empirical Bayesian spatial prediction using wavelets. In Bayesian Inference in Wavelet Based Model (B. Vidakovic and P. Mueller, eds.). Springer Lecture Notes in Statistics, 141, Springer-Verlag, New York, 203-222.
  • Gabrosek, J., Cressie, N., and Huang, H.-C. (1999). Spatial-temporal prediction of level 3 data for NASA’s Earth Observing System. In Spatial Accuracy Assessment: Land Information Uncertainty in Natural Resources, (K. Lowell ed.). Ann Arbor Press, Chelsea, MI, 331-337.
  • Cressie, N. and Huang, H.-C. (1997). Comment on “On Bayesian analysis of mixtures with an unknown number of components” by S. Richardson and P. Green. Journal of the Royal Statistical Society, Series B, 59, 777.
  • Huang, H.-C. and Cressie, N. (1996). Spatio-temporal prediction of snow water equivalent using the Kalman filter. Computational Statistics and Data Analysis, 22, 159-175. (Abstract, Postscript, PDF)