Publications | Hsin-Cheng Huang

Chin, Y.-S., Hsu, N.-J., and Huang, H.-C. (2024). Covariate-dependent spatio-temporal covariance models, Spatial Statistics, to appear.
Wang, P. K., Lin, K.-H. E., Lin, Y.-S., Lin, H.-J., Pai, P.-L., Tseng, W.-L., Huang, H.-C., and Lee, C.-R. (2024). Reconstruction of the temperature index series of China in 1368-1911 based on REACHES database, Scientific Data, 11, 1117.
Tzeng, S, Chen, B.-Y., and Huang, H.-C. (2024). Assessing spatial stationarity and segmenting spatial processes into stationary components, Journal of Agricultural, Biological, and Environmental Statistics, 29, 301-319.
Hsu, N.-J., Huang, H.-C., Tsay, R. S., and Kao, T.-C. (2024). Rank-R matrix autoregressive models for modeling spatio-temporal data, Statistics and its Interface, 17, 275-290.
Tzeng, S, Lai, C.-W., and Huang, H.-C. (2023). Spatially adaptive calibrations of AirBox PM2.5 data, Biometrics, 79, 3637-3649.
Chang, C.-H., Huang, H.-C., and Ing, C.-K. (2023). Selection of linear mixed-effects models for clustered data, Scandinavian Journal of Statistics, 50, 875-897.
Lin, D.-C., Huang, H.-C., and Tzeng, S. (2023). Some enhancements to deepkriging, Stat, e559.
Huang, H.-C. and Hsu, N.-J. (2022). Regularized principal component analysis, In Wiley StatsRef: Statistics Reference Online (eds N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri and J.L. Teugels), https://doi.org/10.1002/9781118445112.stat08403.
Fan, Y.-T. and Huang, H.-C. (2022). Spatially varying coefficient models using reduced-rank thin-plate splines, Spatial Statistics, 51, 100654.
Chang, C.-H., Huang, H.-C., Ing, C.-K. (2022). Inference of random effects for linear mixed-effects models with a fixed number of clusters, Annals of the Institute of Statistical Mathematics, 74, 1143-1161.
Hsu, N.-J., Huang, H.-C., Tsay, R. S. (2021). Matrix autoregressive spatio-temporal models, Journal of Computational and Graphical Statistics, 30, 1143-1155.
Huang, H.-C., Cressie, N., Zammit-Mangion, A., and Huang, G. (2021). False discovery rates to detect signals from incomplete spatially aggregated data, Journal of Computational and Graphical Statistics, 30, 1081-1094.
Yan, Y, Huang, H.-C., and Genton, M. G. (2021). Vector autoregressive models with spatially structured coefficients for time series on a spatial grid, Journal of Agricultural, Biological, and Environmental Statistics, 26, 387-408.
Huang, S.-H., Huang, H.-C., Tsay, R. S., and Pan, G.-M. (2021). Testing independence between two spatial random fields, Journal of Agricultural, Biological, and Environmental Statistics, 26, 161-179.
Lai, C.-W. and Huang, H.-C. (2020). Intensity estimation of spatial point processes based on area-aggregated data, Japanese Journal of Statistics and Data Science, 3, 413-428.
Shen, X. and Huang, H.-C. (2020). Discussion of “From fixed-X to random-X regression: bias-variance decompositions, covariance penalties, and prediction error estimation, Journal of the American Statistical Association, 115, 152-156.
Huang, G., Chen, L.-J., Hwang, W.-H., Tzeng, S. and Huang, H.-C. (2018). Real-time PM2.5 mapping and anomaly detection from AirBoxes in Taiwan, Environmetrics, 29.
Tzeng, S. and Huang, H.-C. (2018). Resolution adaptive fixed rank kriging, Technometrics, 60, 198-208 (R package autoFRK).
Wang, W.-T. and Huang, H.-C. (2018). Regularized spatial maximum covariance analysis. Environmetrics, 29 (R package SpatMCA).
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 SpatPCA).
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 FGSG).
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, 105, 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 EFDR).
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)