Publications

Note: * means PhD student as first author.

IN-PREPARATION 2020

*Wang Y, Ting CM and Ombao H. (2020). Exploratory Methods for High Dimensional Time Series with Applications to Brain Signals. Journal of Statistical Software, In Preparation.

This paper won an award in the ENAR 2017 Student Paper Competition.

Das S, Ombao H and Nason G. (2020). Dissimilarity of sample covariance of periodograms, a

measure for temporal non-stationarity and clustering of time series. In Preparation.

Jiao S, *Shen T, *Gao X, Yu Z and Ombao H. (2020). Change-Point Detection of Multivariate Time Series With Application to Image Analysis. In Preparation.

*Granados-Garcia G, Fiecas M, Shahbaba B, Fortin N and Ombao H. (2020). Modeling brain waves as a mixture of latent processes. In Preparation. This paper won an award in the ENAR 2020 Student Paper Competition.

*Zhu Y, Fontaine C, Frostig R and Ombao H. (2020). Analysis of Multivariate Non-stationary Time Series Using Copula-based Dependence Measures. In Preparation.

*Guerrero M, Huser R and Ombao H. (2020). Modeling the Impact of Extreme Behavior on a Network of Time Series. In Preparation.

*Kazimierska K, Dutta C, Dudek A and Ombao H. (2020). Cross-Frequency Coupling in Bivariate Time Series. In Preparation.

Chao F, Gerland P, Cook AR and Alkema L. (2020). Scenario-based Bayesian probabilistic projections of the sex ratio at birth and missing female births for all countries. In Preparation.

Chao F, You D, Hug L, Pedersen J, Ombao H and Alkema L. (2020). A Systematic Assessment of National Under-5 Mortality Rate by Place of Residence for 109 Countries. In Preparation.

Masquelier B, Chao F, You D and Alkema L. (2020). Estimation for children mortality rate aged 5-24 and disaggregates by sex for all countries. In Preparation.

Dutta CN and Ombao H. (2020). A Thorough Investigation of Inter-hemispheric Asymmetry Patterns in the ADHD Brain With Pharmaceutical Intervention. In Preparation.

Dutta CN, Merecek J, Mareckova K, Dutta A, Ombao H. (2020). Low rank plus sparse matrix decomposition for the dynamic assessment of fMRI signals using the ADHD-200. In Preparation.

SUBMITTED 2020

Jiao S and Ombao H. (2020). Shape-Preserving Prediction for Stationary Functional Time Series. Submitted. arXiv:1910.12046

*Pluta D, Ombao H, Chen C, Xue G, Moyzis R and Yu Z. (2020). Adaptive Mantel Tests with Applications to Neuroimaging Data. Submitted. https://arxiv.org/abs/1712.07270

Sundararajan R, Frostig RD, Ombao H. (2020). Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals. Canadian Journal Statistics, Submitted. https://arxiv.org/abs/1911.12295

Jiao S, Aue A and Ombao H. (2020). Functional time series prediction under partial observation of the future curve. Submitted. arXiv:1906.00281

Jiao S, Frostig RD and Ombao H. (2020). Classification of functional data by detecting the discrepancy of second moment structure of scaled functions. Submitted. arXiv:2004.00855

*Guerrero M, Barreto-Souza W and Ombao H. (2020). Integer-valued autoregressive process with flexible marginal and innovation distributions. Submitted. arXiv:2004.08667

Maia G, Barreto-Souza W, Bastos F and Ombao H. (2020). Semiparametric time series models driven by latent factor. International Journal of Statistics, Submitted. https://arxiv.org/pdf/2004.11470.pdf

Embleton J, Knight M and Ombao H. (2020). Multiscale modelling of replicated nonstationary time series. Journal of the Royal Statistical Society Series B, Submitted. http://arxiv.org/abs/2005.09440

Chao F, Guilmoto CZ, KC S and Ombao H. (2020). Probabilistic Projection of the Sex Ratio at Birth and Missing Female Births by States and Union Territories in India. PLOS One, Submitted. https://arxiv.org/pdf/2004.02228

UNDER REVISION 2020

(100.) Ting CM, Ombao H and Sh-Hussein. (2020). Multi-Scale Factor Analysis of High-Dimensional Brain Signals. IEEE Transactions on Network Science and Engineering, [First revision submitted].

(101.) Yu CH, Prado R, Ombao H and Rowe D. (2020). Bayesian spatial modeling via kernel convolutions on complex-valued FMRI signals. Annals of Applied Statistics, [Under revision].

(102.) *Ngo D, Lopour B, Sun Y, Shrey D, Smith R and Ombao H. (2020). A Geometric Approach to Visualization and Testing of Coherence Matrices of Multivariate Time Series. Annals of Applied Statistics, [Under revision].

(103.) *Gao X, Shen W and Ombao H. (2020). Penalized Probabilistic Matrix Data Clustering and Its Application to Analyzing Local Field Potentials. Biometrics, [First revision submitted].

(104.) Barreto-Souza W and Ombao H. (2020). Negative Binomial Autoregression: A Tractable Model with Composite Likelihood-Based Inference. Scandinavian Journal of Statistics, [First revision submitted].

(105). *Chen T, Sun Y, Euan C and Ombao H. (2020). Clustering Brain Signals: A Robust Approach Using Functional Data Ranking. Journal of Classification, [First revision submitted].

PUBLISHED 2020

(94.) Smith R, Ombao H, Schrey D and Lopour B. (2020). Inference on Long-Range Temporal Correlations in Human EEG Data. IEEE Journal of Biomedical and Health Informatics, 24(4):1070-1079. doi: 10.1109/JBHI.2019.2936326

(95.) Gorshkov O and Ombao, H. (2020+). Evaluation of monofractal and multifractal properties of inter beat (R-R) intervals in cardiac signals for differentiation between the normal and pathology classes. IET Signal Processing. 10.1049/iet-spr.2018.5536.

(96.) Fontaine C, Frostig R and Ombao H. (2020+). Modeling dependence via copula functionals of Fourier coefficients. TEST, Accepted for Publication.

(97.) Li L, Pluta D, Shahbaba B, Fortin N, Ombao H and Baldi P. (2020+). Modeling Dynamic Functional Connectivity Using Gaussian Latent Processes. NIPS, Accepted for Publication.

(98.) Huang SG, Samdin B, Ting CM, Ombao H and Chung M. (2020+). Statistical Model for Dynamically-Changing Correlation Matrices With Application to Brain Connectivity. Journal of Neuroscience Methods, 331, 108480.

(99.) *Hu L, Guindani M, Fortin N and Ombao H. (2020+). Hierarchical Bayesian Model for Differential Connectivity. Econometrics and Statistics, Accepted for Publication.

(100.) Guilmoto CZ, Chao F and Kulkarni PM. (2020+). On the estimation of female births missing due to prenatal sex selection. Population Studies, Accepted for Publication.

(101.) Moraga P, Ketcheson DI, Ombao HC and Duarte CM. (2020+). Assessing the age- and gender-dependence of the severity and case fatality rates of COVID-19 disease in Spain. Wellcome Open Research, Accepted for Publication.

PUBLISHED 2019

(73.) *Schroeder A and Ombao H. (2019). FreSpeD: Frequency-Specific Change-Point Detection Method in Multi-Channel Epileptic Seizure EEG Data. Journal of the American Statistical Association, 114: 115-128.

(74.) *Chau J, Ombao H and von Sachs R. (2019). Intrinsic data depth for Hermitian positive definite matrices. Journal of Computational and Graphical Statistics, 28:2, 427- 439. DOI: 10.1080/10618600.2018.1537926

(75.) Shrey D, Smith R, Kim O, Ombao H, Hussain H and Lopour B. (2018). Strength and stability of functional networks reflect treatment. Clinical Neurophysiology, 129: 2137-2148

(76.) *Gao X, Ombao H and Gillen D. (2018). Fisher Information Matrix of Binary Time Series. Metron, 76, 287-304.

(77). *Gao X, Shahbaba B, Fortin N and Ombao H. (2019). Evolutionary State-Space Models With Applications to Time-Frequency Analysis of Local Field Potentials. Statistica Sinica, Accepted for publication. https://doi.org/10.5705/ss.202017.0420 This paper won an award in the ENAR 2017 Student Paper Competition - This paper won an award in the JSM 2017 Student Paper Competition.

(78.)  Yu, Z.Prado, R.Cramer, S.Quinlan, E. and Ombao, H. (2019), "A Bayesian Model for Activation and Connectivity in Task-related fMRI Data", Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A (Advances in Econometrics, Vol. 40A), Emerald Publishing Limited, pp. 91-132. https://doi.org/10.1108/S0731-90532019000040A006

(79.) *Cruz M, Gillen D, Bender M and Ombao H. (2019). Assessing Health Care Interventions via an Interrupted Time Series Model: Study Power and Design Considerations. Statistics in Medicine, 10, 1734-1752.

(80.) Euan C, Sun Y and Ombao H. (2019). Coherence-based time series clustering for brain connectivity visualization. Annals of Applied Statistics, 13, 990-115.

(81.) Chung MK, Lee H, DiChristofano A, Ombao H and Solo V. (2019). Exact topological inference of the resting-state brain networks in twins. Network Neuroscience, 3(3), 674–694. https://doi.org/10.1162/ netn_a_00091

(82.) Phang CR, Ting CM, Samdin SB and Ombao H. (2019). Classification of EEG-based effective brain connectivity in schizophrenia using deep neural networks. 9th International IEEE EMBS Conference on Neural Engineering, NER 401-406.

(83.) Wang Y and Ting CM, Gao X and Ombao H. (2019). Exploratory analysis of brain signals through low dimensional embedding. 9th International IEEE EMBS Conference on Neural Engineering, NER 997-1002.

(84.) Gao X, Shen W, Hu J, Fortin N and Ombao H. (2019). Modeling Local Field Potentials with Regularized Matrix Data Clustering. 9th International IEEE EMBS Conference on Neural Engineering, NER 597-602.

(85.) Fuad N,  Ting CM, Sh-Hussain S and and Ombao H. (2019). Short-segment heart sound classification using an ensemble of deep convolutional neural networks. 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, 1318-1322.

(86.) Wang Y, Ombao H and Chung M. (2019). Statistical Persistent Homology of Brain Signals. 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, 1125-1129.

(87.) Gao X, Shen W, Ting CM, Cramer S, Srinivasan R and Ombao H. (2019). Estimating brain connectivity using copula Gaussian graphical models. IEEE International Symposium on Biomedical Imaging, Venice, 108-112.

(88.) Samdin SB, Ting CM and Ombao H. (2019). Detecting state changes in community structure of functional brain networks using a Markov-switching stochastic block model. IEEE International Symposium on Biomedical Imaging, Venice, 1483-1487.

(89.) Krokos G, Papadopoulos VP, Sofianos SS, Ombao H, Dybczak P and Hoteit I. (2019). Natural climate oscillations may counteract Red Sea warming over the coming decades. Geophysical Research Letters, 46, 3454– 3461. https://doi.org/10.1029/2018GL081397

(90.) Fuad N,  Sh-Hussain S, Ting CM,  Samdin S, Ombao H and Hussain H.  (2019). A Markov-Switching Model Approach to Heart Sound Segmentation and Classification. IEEE Journal of Biomedical and Health Informatics, 24(3):705-716. DOI: 10.1109/JBHI.2019.2925036

(91.) Phang, CR, Fuad N, Hussain H, Ting CM  and Ombao, H. (2019). A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia from EEG Connectivity Patterns. IEEE Journal of Biomedical and Health Informatics. PP. 1-1. 10.1109/JBHI.2019.2941222.

(92.) Fontaine C, Frostig R and Ombao H. (2019). Modeling non-linear spectral dependence using copulas with applications to rat local field potentials. Econometrics and Statistics, In Press. https://doi.org/10.1016/j.ecosta.2019.06.003

(93.) Lan S, Holbrook A, Fortin N, Ombao H and Shahbaba B. (2019). Flexible Bayesian Dynamic Modeling of Covariance and Correlation Matrices. Bayesian Analysis, In Press. doi:10.1214/19-BA1173.

PUBLISHED 2018

(56.)  Vandenberg-Roche A and Ombao H. (2018). 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.

(57.) *Euan C, Ombao H and Ortega J. (2018). The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering. Journal of Classification, 35, 71-99.

(58.) *Gao X, Ombao H and Shahbaba B. (2018).  Predicting Sleep Stages Using Gaussian Processes. Journal of Classification, 35, 549-579.

(59.) *Wang Y, Ombao H and Chung M. (2018). Persistence Landscape with Application to Epileptic Seizure Encephalogram Data. Annals of Applied Statistics, 12 ,1506-1534. This paper won an award in the ENAR 2017 Student Paper Competition.

(60.) Ombao H, Fiecas M, Ting CM and Low YF. (2018). Statistical Models for Brain Signals with Properties that Evolve Across Trials. NeuroImage, 180(Pt B):609-618.

(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, 74(3):823-833.

(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, 37, 1011-1023.

(63.) *Hu L, Fortin N and Ombao H. (2018). Vector Autoregressive Models for Multivariate Brain Signals. Statistics in the Biosciences, Accepted for Publication.

(64.) *Park T, Eckley I and Ombao H. Dynamic Classification Using Multivariate Locally Stationary Wavelets. (2018). Signal Processing, 152, 118-129.

(65.) Pluta D, Tong S, Yu Z, Chen C, Gui and Ombao H. (2018). Big Data in the Brain Sciences. Statistics and Probability Letters, 136, 83-86. https://doi.org/10.1016/j.spl.2018.02.048.

(66.) Euan C, Ombao H and Ortega J. (2018). Spectral Synchronicity in Brain Signals. Statistics in Medicine, 37, 2855-2873.

(67.) *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, 67, 1103-1145.

(68.) 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, 67, 1103-1145.

(69.) *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, 113:524, 1395 1410, DOI: 10.1080/01621459.2018.1476244 This paper won an award in the JSM 2017 Student Paper Competition.

(70.) *Gorrostieta C, Ombao H and von Sachs R. (2018). Time-Dependent Dual Frequency Coherence in Multivariate Non-Stationary Time Series. Journal of Time Series Analysis, 40, 3-22.

(71.) *Gao X, Ombao H and Gillen D. (2018). Fisher Information Matrix of Binary Time Series. Metron, 76, 287-304.

(72.) Fuad N,  Ting CM, Sh-Hussain S and and Ombao H. (2018). Short-segment heart sound classification using an ensemble of deep convolutional neural networks. 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, 1318-1322.

PUBLISHED 2017

(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.

(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.

(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.

(55.) *Cruz M, Bender M and Ombao H. (2017). Robust Interrupted Time Series Models for Analyzing Complex Healthcare Interventions.  Statistics in Medicine, 20, 36(29), 4660-4676. This paper won an award in the JSM 2017 Student Paper Competition.

BEFORE 2017

[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)

[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)

[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.

[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)