Publications

Submitted or in Preparation

  • [88] *Gao X, Shen W and Ombao H. (2018). Penalized Probabilistic Matrix Data Clustering and Its Application to Analyzing Local Field Potentials. In Preparation.
  • [87] *Shen T, *Gao X, Yu Z and Ombao H. (2018). Change-Point Detection of High Dimensional Time Series. In Preparation.
  • [86] *Hu L, Guindani M, Fortin N and Ombao H. (2018). Hierarchical Bayesian Model for Multi-Trial Brain Signals. In Preparation.
  • [85] *Ngo D, Lopour B, Sun Y, Shrey D, Smith R and Ombao H. (2018). A Geometric Approach to Visualization and Testing of Coherence Matrices of Multivariate Time Series. In Preparation.
  • [84] *Chau J, Ombao H and von Sachs R. (2018). Intrinsic data depth for Hermitian positive definite matrices. Journal of Computational and Graphical Statistics, Submitted.
  • [83] Barreto-Souza W and Ombao H. (2018). Negative Binomial Autoregression: A Tractable Model with Composite Likelihood-Based Inference. Biometrika, In Preparation.
  • [82] Das S, Ombao H and Nason G. (2018). Dissimilarity of sample covariance of periodograms, a measure for temporal non-stationarity and clustering of time series. In Preparation.
  • [81] *Pluta D, *Shen T, Yu Z, Chen C and Ombao H. (2018). Adaptive Mantel Tests with Applications to Neuroimaging Data. Journal of the American Statistical Association, In Preparation.
  • [80] *Cruz M, Gillen D, Bender M and Ombao H. (2018). Assessing Health Care Interventions via an Interrupted Time Series Model: Study Power and Design Considerations. Statistics in Medicine, Submitted.
  • [79] Fontaine C and Ombao H. (2018). Dependence Between Time Series Using Spectral-BasedMeasures Within Copula Inference. Statistics and Probability Letters, Submitted.
  • [78] *Yu Z, Ombao H, Prado R, Burke E and Cramer S. (2017). A Bayesian Model for Activation and Connectivity in Task-related fMRI Data. Advances in Econometrics, (Submitted). 
  • [77] *Sun Z and Ombao H. (2017). Short-Term Traffic Prediction Using Mixed Autoregressive Models.In Preparation. 
  • [76] Ting CM, Ombao H and Sh-Hussein. (2017). Multi-Scale Factor Analysis of High-Dimensional Brain Signals. IEEE Transactions on Biomedical Engineering, (Submitted).
  • [75] *Wang Y, Ting CM and Ombao H. (2017). Exploratory Methods for High Dimensional Time Series with Applications to Brain Signals. Journal of Statistical Software, In Preparation. [Distinguished Student Paper Award ENAR 2017].
  • [74] *Gao X, Ombao H and Gillen D. (2017). Fisher Information Matrix of Binary Time Series. Electronic Journal of Statistics, (Submitted).

‌Submitted (Under Revision)

  • [73] *Gao X, Shahbaba B, Fortin N and Ombao H. (2017). Evolutionary State-Space Models With Applications to Time-Frequency Analysis of Local Field Potentials. Statistica Sinica, Submitted. [Distinguished Student Paper Award ENAR 2017] and [Distinguished Student Paper Award, American Statistical Association 2017].
  • [72] Shrey D, Smith R, Kim O, Ombao H, Hussan H and Lopour B. (2017). Strength and stability of functional networks reflect treatment. Epilepsia, [First revision submitted]. 
  • [71] *Gorrostieta C, Ombao H and von Sachs R. (2017). Time-Dependent Dual Frequency Coherence in Multivariate Non-Stationary Time Series. Journal of Time Series Analysis [Fourth revision submitted].

Published

  • [70] *Yu CH, Prado R, Ombao H and Rowe D. (2018). A Bayesian Variable Selection Approach Yields Improved Brain Activation From Complex-Valued fMRI. Journal of the American Statistical Association, Accepted for Publication.
  • [69] Ombao H and Ting CM. (2018). Discussion of “The statistical analysis of acoustic phonetic data: exploring differences between spoken Romance languages” by Pigoli, Hadjipantelis, Coleman and Aston. Journal of the Royal Statistical Society, Series A, Accepted for Publication.
  • [68] *Gao X, Shen W and Ombao H. (2018). Discussion of “The statistical analysis of acoustic phonetic data: exploring differences between spoken Romance languages” by Pigoli, Hadjipantelis, Coleman and Aston. Journal of the Royal Statistical Society, Series A, Accepted for Publication.
  • [67] *Schroeder A and Ombao H. (2018). FreSpeD: Frequency-Specific Change-Point Detection Method in Multi-Channel Epileptic Seizure EEG Data. Journal of the American Statistical Association, Accepted for Publication.
  • [66] Euan C, Ombao H and Ortega J. (2018). Spectral Synchronicity in Brain Signals. Statistics in Medicine, Accepted for Publication.
  • [65] Pluta D, Tong S, Yu Z, Chen C, Gui and Ombao H. (2018). Big Data in the Brain Sciences. Statistics and Probability Letters, Accepted for Publication.
  • [64] *Park T, Eckley I and Ombao H. Dynamic Classification Using Multivariate Locally Stationary Wavelets. (2018). Signal Processing, Accepted for Publication.
  • [63] *Hu L, Fortin N and Ombao H. (2018). Vector Autoregressive Models for Multivariate Brain Signals. Statistics in the Biosciences, Accepted for Publication.
  • [62] Ting CM, Ombao H and Sh-Hussein. (2018). Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models. IEEE Transactions on Medical Imaging, Accepted for Publication.
  • [61] Castruccio S, Ombao H and Genton M. (2018). A Scalable Multi-Resolution Spatio-Temporal Model for Brain Activation and Connectivity in fMRI data. Biometrics, In Press.
  • [60] Ombao H, Fiecas M, Ting CM and Low YF. (2018). Statistical Models for Brain Signals with Properties that Evolve Across Trials. NeuroImage, In Press.
  • [59] *Wang Y, Ombao H and Chung M. (2018). Persistence Landscape with Application to Epileptic Seizure Encephalogram Data. Annals of Applied Statistics, In Press. [ENAR 2015 Student Paper Award].
  • [58] *Gao X, Ombao H and Shahbaba B. (2017). Predicting Sleep Stages Using Gaussian Processes. Journal of Classification, In Press.
  • [57] *Cruz M, Bender M and Ombao H. (2017). Robust Interrupted Time Series Models for Analyzing Complex Healthcare Interventions. Statistics in Medicine, In Press. (click here). [Distinguished Student Paper Award, American Statistical Association 2017].
  • [56] *Euan C, Ombao H and Ortega J. (2017). The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering. Journal of Classification, In Press.
  • [55] Vandenberg-Roche A and Ombao H. (2017). Discussion of “Should we sample a time series more frequently?: Decision support via multirate spectrum estimation” by G P Nason, B Powell, D Elloitt and P Smith. Journal of the Royal Statistical Society, Series A. In Press.
  • [54] Kang H, Ombao H, Fonnesbeck C and Morgan V. (2017). A Bayesian Double Fusion Model for Resting State Brain Connectivity Using Joint Functional and Structural Data. Brain Connectivity, 7(4), 219-227. (click here)
  • [53] *Balqis S, Ting CM, Ombao H and Sh-Salleh. (2017). A Unified Estimation Framework for State-Related Changes in Effective Brain Connectivity. IEEE Transactions on Biomedical Engineering, 64(4), 844-858. (click here)
  • [52] *Sayal H, Aston J, Duncan E and Ombao H. (2017). An Introduction to Wavelet Benchmarking with Application to Seasonal Adjustment. Journal of the Royal Statistical Society Series A,180(3), 863-889. (click here)
  • [50] Fiecas M and Ombao H. (2016). Modeling the Evolution of Dynamic Brain Processes During an Associative Learning Experiment. Journal of the American Statistical Association, 111, 1440-1453. (click here)
  • [51] *Wang Y, Ting CM and Ombao H. (2016). Modeling Effective Connectivity in High Dimensional Source Signals From EEG. IEEE Journal of Selected Topics in Signal Processing, 10, 1315-1325. (click here)
  • [49] *Yu Z, Prado R, Burke E, Cramer S and Ombao H. (2016). A Hierarchical Bayesian Model for Studying the Impact of Stroke on Brain Motor Function. Journal of the American Statistical Association, 111, 549-563. (click here)
  • [48] *Zhou B, Moorman D, Behseta S, Ombao H and Shababa B. (2016). A Dynamic Bayesian Model for Characterizing Cross Neuronal Interactions During Decision Making. Journal of the American Statistical Association, 111, 459-471. (click here)
  • [47] *Kang H, Blume J, Ombao H and Badre D. (2015). Simultaneous Control of Error Rates in fMRI Data Analysis. NeuroImage, 123, 102-113. (click here)
  • [46] *Ngo D, Sun Y, Genton M, Wu J, Cramer SC, Srinivasan R and Ombao H. (2015). An Exploratory Data Analysis of Electroencephalograms Using the Functional Boxplots Approach. Frontiers in Neuroscience: Brain Imaging Methods, 9, 282. (click here)
  • [Distinguished Student Paper Award, ENAR 2015].
  • [45] Kirch C, *Muhsal B and Ombao H. (2015). Detection of Changes in Multivariate Time Series With Application to EEG data. Journal of the American Statistical Association, 110, 1197-1216. (click here)
  • [44] *Park, T, Eckley I and Ombao H. (2014). Estimating the time-evolving partial coherence between signals via multivariate locally stationary wavelet processes. IEEE Transactions on Signal Processing, 62, 5240-5250. (click here)
  • [43] Shahbaba B, *Zhou B, Lan S, Ombao H, Moorman D and Behseta S. (2014). A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons. Neural Computation, 26, 9,2025-2051. (click here)
  • [42] *Gorrostieta C, Fiecas M, Ombao H, Burke E and Cramer S. (2013). Hierarchical Vector Auto-Regressive Models and Their Applications to Multi-Subject Effective Connectivity. Frontiers in Computational Neuroscience, 7: 159, 1-11. (click here)
  • [41] *Koestler D, Ombao H and Bender J. (2013). Ensemble-based methods for forecasting census in
  • hospital units. BMC Medical Research Methodology, 13:67, 1-12. (click here) [Distinguished Student Paper Award, ENAR 2011].
  • [40] Olhede S and Ombao H. (2013). Covariance of Replicated Modulated Cyclical Time Series. IEEE Transactions on Signal Processing, 61, 1944-1957. (click here)
  • [39] Fiecas M, Ombao H, van Lunen D, Baumgartner R, Coimbra A and Feng D (2013). Quantifying Temporal Correlations: A Test-Retest Evaluation of Functional Connectivity in Resting-State fMRI. NeuroImage, 65, 231-241. (click here)
  • [38] *Kang H, Ombao H, Linkletter C, Long N and Badre D. (2012). Spatio-Spectral Mixed Effects Model for Functional Magnetic Resonance Imaging Data. Journal of the American Statistical Association, 107, 568-577. (click here) [John Van Ryzin Award, ENAR 2011].
  • [37] Ombao H. (2012). Time Series Analysis of multivariate non-stationary time series using the localised Fourier Library. Handbook of Statistics: Time Series, Elsevier Science. 
  • [36] Stoffer D and Ombao H. (2012). Editorial: Special Issue on Time Series Analysis in the Biological Sciences. Journal of Time Series Analysis, 33(5), 701-703. (click here)
  • [35] Ombao H. (2012). Discussion of “Time–Threshold Maps: Using information from wavelet reconstruction with all threshold values simultaneously” by P. Fryzlewicz. Journal of the Korean Statistical Society, 41, 171-172. (click here)
  • [34] Motta, G. and Ombao, H. (2012). Evolutionary Factor Analysis of Replicated Time Series. Biometrics, 68, 825-836. (click here)
  • [33] *Gorrostieta C, Ombao H, Prado R, Patel S and Eskandar E. (2012). Exploring Dependence Between Brain Signals in a Monkey During Learning. Journal of Time Series Analysis, 33(5), 771-778. (click here)
  • [32] *Gorrostieta C, Ombao H, Bedard P and Sanes J.N. (2012). Investigating Stimulus-Induced Changes in Connectivity Using Mixed Effects Vector Autoregressive Models. NeuroImage, 59, 3347-3355. (click here)
  • [31] Verducci J and Ombao H. (2011). Introduction to the special issue on best papers from the SLDM competition. Statistical Analysis and Data Mining, 4: 565-566. (click here)
  • [30] Bunea F, She Y Ombao H, Gongvatana W, Devlin K and Cohen R. (2011). Penalized Least Squares Regression Methods and Applications to Neuroimaging. NeuroImage, (55), 1519-1527. (click here)
  • [29] * Fiecas, M. and Ombao, H. (2011). The Generalized Shrinkage Estimator for the Analysis of Functional Connectivity of Brain Signals. Annals of Applied Statistics, 5, 1102-1125. (click here) [Student Paper Award, New England Statistics Symposium 2010].
  • [28] *Fiecas, M., Ombao, H., Linkletter, C., Thompson, W. and Sanes, J.N. (2010). Functional Connectivity: Shrinkage Estimation and Randomization Test. NeuroImage, (40), 3005-3014. (click here)
  • [27] *Freyermuth, J-M., Ombao, H. and von Sachs, R. (2010). Spectral Estimation from Replicated Time Series: An Approach Using the Tree-Structured Wavelets Mixed Effects Model. Journal of the American Statistical Association, 105, 634-646. (click here)
  • [26] *Bohm, H., Ombao, H., von Sachs, R. and Sanes, J.N. (2010). Discrimination and Classification of Multivariate Non-Stationary Signals: The SLEX-Shrinkage Method. Invited for the Special Issue on Time Series (In Honor of Emmanuel Parzen), Journal of Statistical Planning and Inference, (140), 3754-3763. (click here)
  • [25] Ombao, H. and Prado, R. (2010). A Closer Look at the Two Approaches for Clustering and Classification of Non-Stationary Time Series. In Statistical Methods for Modeling Human Dynamics: An Inter-Disciplinary Dialogue. Taylor and Francis
  • [24] *Gao, B., Ombao, H. and Ho, R. (2010). Cluster Analyis for Non-Stationary Time Series. In Statistical Methods for Modeling Human Dynamics: An Inter-Disciplinary Dialogue (pp. 85-122),Taylor and Francis.
  • This document has been edited with the instant web content composer. The online instant HTML converter make a great resource that will help you a lot in your work. Save this link or add it to your bookmarks.
  • [23] Fryzlewicz, P and Ombao, H. (2009). Consistent Classification of Non-Stationary Signals Using Stochastic Wavelet Representations, Journal of the American StatisticalAssociation, 104, 299-312. (click here)
  • [22] Tadjuidje,J, Ombao, H. and Davis, R. (2009). A Class of Switching Regimes Autoregressive Driven Processes with Exogenous Components.Journal of Time Series Anal, 30, 505-533. (click here)
  • [21] Shitan, M., Ombao, H. and Ling, K-W. (2009). Spatial Modeling of Peak Frequencies of Brain Signals. Malaysian J Math Sci, 3(1), 13-26. 
  • [20] Ombao, H, Shao, X., Rykhlevskaia, E, Fabiani, M and Gratton, G. (2008). Spatio-Spectral Analysis of Brain Signals, Statistica Sinica, 18, 1465-1482. (click here)
  • [19] *Ho, M, Ombao, H, Edgar, C and Miller, G. (2008). Time-Frequency Discriminant Analysis of MEG Signals, NeuroImage, 40(1), 174-186. (click here)
  • [18] Ombao, H. and Van Bellegem (2008). Coherence Analysis: A Linear Filtering Point Of View, IEEE Transactions on Signal Processing, 56(6), 2259-2266. (click here)
  • [17] *Choi, H., Ombao, H. and Ray, B. (2007). Sequential Change-point Detection Method in Time Series, Technometrics, 50(1), 40-52. (click here)
  • [16] Ombao, H. and Ho, M. (2006). Time-dependent frequency domain principal components analysis of multi-channel non-stationary signals, Comp Stat and Data Anal, 50(9), 2339-2360. (click here)
  • [15] *Shinkareva, S., Ombao, H. and Sutton B. (2006). A Data-Driven Approach to Classification and Discrimination of fMRI Data, NeuroImage, 33, 63-71.
  • [14] *Ho, M., Shumway, R. and Ombao, H. (2006) State-Space Models for Longitudinal Data with applications in the Biological and Social Sciences. , In Walls and Shafer (eds.) Models for Intensive Longitudinal Data. New York, NY: Oxford Univ. Press
  • [13] Bunea, F., Ombao, H. and Auguste, A. (2006). Minimax Adaptive Spectral Estimation from an Ensemble of Signals, IEEE Transactions on Signal Processing, 54, 2865-2873 (click here)
  • [12] Ombao, H., von Sachs, R. and Guo, W. (2005). SLEX Analysis of Multivariate Non-Stationary Time Series, Journal of the American StatisticalAssociation, 100, 519-531. (click here)
  • [11] *Gamalo M, Ombao H and Jennings R. (2005). Comparing Extent of Activation: A Robust Permutation Approach. NeuroImage, 24(3): 715-722. (click here)
  • [10] *Ho, M., Ombao, H. and Shumway, R. (2005). Modelling Brain Dynamics: A State-Space Approach, Statistica Sinica, 15, 407-425. (click here)
  • [9] *Huang, H., Ombao, H. and Stoffer D. (2004). Classification and Discrimination of Non-Stationary Time Series Using the SLEX Model, Journal of the American StatisticalAssociation,99, 763-774. (click here)
  • [8] Ombao, H., Heo, J., Stoffer, D. (2004). Statistical Analysis of Seismic Signals: An Almost Real Time Approach, Time Series Analysis and Applications to Geophysical Systems (eds. D. Brillinger E. Robinson and F. Schoenberg), New York: Springer Verlag, IMA Series, 139, 53-72. (click here)
  • [7] *Pasia, J., Hermosilla, A. and Ombao, H. (2004). Genetic Algorithms: Useful Statistical Tools,Journal of Statistical Computation and Simulation, 75, 237-251. (click here)
  • [6] Guo, W., Da, M., Ombao, H. and von Sachs (2003). Smoothing Spline ANOVA For Time-Dependent Spectral Analysis, Journal of the American Statistical Association, 98, 643-652. (click here)
  • [5] Stoffer, D., Ombao, H. and Tyler, D. (2002). Evolutionary Spectral Envelope: An Approach Using the Tree-Based Adaptive Segmentation, Annals of the Institute of Statistical Mathematics, 54, 201-223. (click here)
  • [4] Ombao, H., Raz, J., von Sachs, R. and Guo, W. (2002). The SLEX Model of Non-Stationary Random Process, Annals of the Institute of Statistical Mathematics, 54, 171-200. (click here)
  • [3] Ombao, H., Raz, J., Strawderman, R. and von Sachs, R. (2001). A simple GCV method of span selection for periodogram smoothing, Biometrika, 88, 1186-1192. (click here)
  • [2] Ombao, H., Raz, J., von Sachs, R. and Malow, B. (2001). Automatic Statistical Analysis of Bivariate Non-Stationary Time Series, Journal of the American Statistical Association, 96, 543-560 (click here)
  • [1] Stoffer, D. and Ombao, H. (2000). Localized Spectral Envelope, Resenhas, 4, 363-381. (click here)
Note: (*) means PhD student as first author.