Note: * means PhD student as first author.

PUBLISHED 2024

(146.) *Granados-Garcia, G., Prado, R., & Ombao, H. (2024). Bayesian Nonparametric Multivariate Mixture of Autoregressive Processes with Application to Brain Signals. Econometrics and Statistics. DOI: 10.1016/j.ecosta.2024.01.004 (click here)

(147.) Khan, H., Khadka, R., Sultan, M. S., Yazidi, A., Ombao, H., & Mirtaheri, P. (2024). Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface. Frontiers in Human Neuroscience, 18, 1354143. DOI: 10.3389/fnhum.2024.1354143 (click here)

(148.) Chow, X.H., Ting, C.M., Wan Hamizan, A.K., Zahedi, F.D., Tan, H.J., Remli, R., Khoo, C.S., Ombao, H., Sahibulddin, S.Z., Husain, S. (2024). Brain waves spectral analysis of human responses to odorous and non-odorous substances: a preliminary study. Journal of Laryngology and Otology, 138 (3), pp. 301-309. DOI: 10.1017/S0022215123000919 (click here)

(149.) Noman, F., Ting, C., Kang, H., Phan, R.C., Ombao, H. (2024). Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification. IEEE Journal of Biomedical and Health Informatics, pp. 1-12. DOI: 10.1109/JBHI.2024.3351177 (click here) PUBLICATION STAGE: Article in Press

PUBLISHED 2023

(128.) *El-Yaagoubi, A.B., Chung, M.K., Ombao, H. (2023). Topological Data Analysis for Multivariate Time Series Data. Entropy, 25 (11), art. no. 1509. DOI: 10.3390/e25111509 (click here)

(129.) Jiao, S., Frostig, R.D., Ombao, H. (2023). Variation pattern classification of functional data. Canadian Journal of Statistics, 51 (4), pp. 943-958. DOI: 10.1002/cjs.11738 (click here)

(130.) Chung, M. K., Das, S., & Ombao, H. (2023). Dynamic topological data analysis of functional human brain networks. Foundations of Data Science, 0-0. DOI: 10.3934/fods.2023013 (click here)

(131.) Chao, F., Masquelier, B., You, D., Hug, L., Liu, Y., Sharrow, D., Rue, H., Ombao, H., Alkema, L., Cao, B., Gaigbe-Togbe, V., Spoorenberg, T., Strong, K.L., Suzuki, E., UN Inter-agency Group for Child Mortality Estimation. (2023). Sex differences in mortality among children, adolescents, and young people aged 0–24 years: a systematic assessment of national, regional, and global trends from 1990 to 2021. The Lancet Global Health, 11 (10), pp. e1519-e1530. DOI: 10.1016/S2214-109X(23)00376-5 (click here)

(132.) Jiao, S., Frostig, R.D., Ombao, H. (2023). Break point detection for functional covariance. Scandinavian Journal of Statistics, 50 (2), pp. 477-512. DOI: 10.1111/sjos.12589 (click here)

(133.) Yu, C.-H., Prado, R., Ombao, H., Rowe, D. (2023). Bayesian spatiotemporal modeling on complex-valued fMRI signals via kernel convolutions. Biometrics, 79 (2), pp. 616-628. DOI: 10.1111/biom.13631 (click here)

(134.) Fokianos, K., Kirch, C., Ombao, H. (2023). Editorial for the special issue on Time Series Analysis. Computational Statistics and Data Analysis, 181, art. no. 107675. DOI: 10.1016/j.csda.2022.107675 (click here)

(135.) Sindhu, K.R., Ngo, D., Ombao, H., Olaya, J.E., Shrey, D.W., Lopour, B.A. (2023). A novel method for dynamically altering the surface area of intracranial EEG electrodes. Journal of Neural Engineering, 20 (2), art. no. 026002. DOI: 10.1088/1741-2552/acb79f (click here)

(136.) *Guerrero, M.B., Huser, R., Ombao, H. CONEX–CONNECT: LEARNING PATTERNS IN EXTREMAL BRAIN CONNECTIVITY FROM MULTICHANNEL EEG DATA. (2023). Annals of Applied Statistics, 17 (1), pp. 178-198. DOI: 10.1214/22-AOAS1621 (click here)

(137.) Piancastelli, L.S.C., Barreto-Souza, W., Ombao, H. (2023). Flexible bivariate INGARCH process with a broad range of contemporaneous correlation. Journal of Time Series Analysis, 44 (2), pp. 206-222. DOI: 10.1111/jtsa.12663 (click here) 

(138.) Maama, M., Jasra, A., Ombao, H. (2023). Bayesian parameter inference for partially observed stochastic differential equations driven by fractional Brownian motion. Statistics and Computing, 33 (1), art. no. 19. DOI: 10.1007/s11222-022-10193-0 (click here)

(139.) *El-Yaagoubi, A.B., Chung, M.K., Ombao, H. (2023). Statistical inference for dependence networks in topological data analysis. Frontiers in Artificial Intelligence, 6, art. no. 1293504. DOI: 10.3389/frai.2023.1293504 (click here) 

(140.) Tan, Y.-F., Ting, C.-M., Noman, F., Phan, R.C.-W., Ombao, H. (2023). A Unified Framework for Static and Dynamic Functional Connectivity Augmentation for Multi-Domain Brain Disorder Classification. Proceedings - International Conference on Image Processing, ICIP, pp. 635-639. DOI: 10.1109/ICIP49359.2023.10222266 (click here) 

(141.) Jasra, A., Maama, M., Ombao, H. (2023). An improved unbiased particle filter. Monte Carlo Methods and Applications. DOI: 10.1515/mcma-2023-2024 (click here) PUBLICATION STAGE: Article in Press

(142.) *El Yaagoubi, A., Ombao, H. (2023). Topological Data Analysis for Directed Dependence Networks of Multivariate Time Series Data. Research Papers in Statistical Inference for Time Series and Related Models: Essays in Honor of Masanobu Taniguchi, pp. 403-417. DOI: 10.1007/978-981-99-0803-5_17 (click here)

(143.) *El Yaagoubi Bourakna, A., Pinto, M., Fortin, N., Ombao, H. (2023). Smooth online parameter estimation for time varying VAR models with application to rat local field potential activity data. Statistics and its Interface, 16 (2), pp. 227-257. DOI: 10.4310/22-SII729 (click here) 

(144.) Piancastelli, L.S.C., Friel, N., Barreto-Souza, W., Ombao, H. (2023). Multivariate Conway-Maxwell-Poisson Distribution: Sarmanov Method and Doubly Intractable Bayesian Inference. Journal of Computational and Graphical Statistics, 32 (2), pp. 483-500. DOI: 10.1080/10618600.2022.2116443 (click here)

(145.) Jiao, S., Aue, A., Ombao, H. (2023). Functional Time Series Prediction Under Partial Observation of the Future Curve. Journal of the American Statistical Association, 118 (541), pp. 315-326. DOI: 10.1080/01621459.2021.1929248 (click here)

PUBLISHED 2022

(113.) Cruz, M., Ombao, H., Gillen, D.L. (2022). A Generalized Interrupted Time Series Model for Assessing Complex Health Care Interventions. Statistics in Biosciences, 14 (3), pp. 582-610. DOI: 10.1007/s12561-022-09346-6 (click here)

(114.) Embleton, J., Knight, M.I., Ombao, H. (2022). MULTISCALE SPECTRAL MODELLING FOR NONSTATIONARY TIME SERIES WITHIN AN ORDERED MULTIPLE-TRIAL EXPERIMENT. Annals of Applied Statistics, 16 (4), pp. 2774-2803. DOI: 10.1214/22-AOAS1614 (click here)

(115.) Chao, F., Kc, S., Ombao, H.(2022). Estimation and probabilistic projection of levels and trends in the sex ratio at birth in seven provinces of Nepal from 1980 to 2050: a Bayesian modeling approach. BMC Public Health, 22 (1), art. no. 358. DOI: 10.1186/s12889-022-12693-0 (click here)

(116.) Degras, D., Ting, C.-M., Ombao, H. (2022). Markov-switching state-space models with applications to neuroimaging. Computational Statistics and Data Analysis, 174, art. no. 107525. DOI: 10.1016/j.csda.2022.107525 (click here)

(117.) Embleton, J., Knight, M.I., Ombao, H. (2022). Wavelet testing for a replicate-effect within an ordered multiple-trial experiment. Computational Statistics and Data Analysis, 174, art. no. 107456. DOI: 10.1016/j.csda.2022.107456 (click here)

(118.) *Granados-Garcia, G., Fiecas, M., Babak, S., Fortin, N.J., Ombao, H. (2022). Brain waves analysis via a non-parametric Bayesian mixture of autoregressive kernels. Computational Statistics and Data Analysis, 174, art. no. 107409. DOI: 10.1016/j.csda.2021.107409 (click here)

(119.) *Dutta, C.N., Christov-Moore, L., Ombao, H., Douglas, P.K. (2022). Neuroprotection in late life attention-deficit/hyperactivity disorder: A review of pharmacotherapy and phenotype across the lifespan. Frontiers in Human Neuroscience, 16, art. no. 938501. DOI: 10.3389/fnhum.2022.938501 (click here)

(120.) Chao, F., Wazir, M.A., Ombao, H. (2022). Levels and trends estimate of sex ratio at birth for seven provinces of Pakistan from 1980 to 2020 with scenario-based probabilistic projections of missing female birth to 2050: A Bayesian modeling approach. International Journal of Population Studies, 8 (2), art. no. 332, pp. 51-70. DOI: 10.36922/ijps.v8i2.332 (click here)

(121.) *Ting, C.-M., Skipper, J.I., Noman, F., Small, S.L., Ombao, H. (2022). Separating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI. IEEE Transactions on Medical Imaging, 41 (6), pp. 1431-1442. DOI: 10.1109/TMI.2021.3139428 (click here)

(122.) Barreto-Souza, W., Ombao, H. (2022). The negative binomial process: A tractable model with composite likelihood-based inference. Scandinavian Journal of Statistics, 49 (2), pp. 568-592. DOI: 10.1111/sjos.12528 (click here)

(123.) Gauran, I.I., Xue, G., Chen, C., Ombao, H., Yu, Z. (2022). Ridge Penalization in High-Dimensional Testing With Applications to Imaging Genetics. Frontiers in Neuroscience, 16, art. no. 836100. DOI: 10.3389/fnins.2022.836100 (click here)

(124.) Noman, F., Yap, S.-Y., Phan, R.C.-W., Ombao, H., Ting, C.-M. (2022). GRAPH AUTOENCODER-BASED EMBEDDED LEARNING IN DYNAMIC BRAIN NETWORKS FOR AUTISM SPECTRUM DISORDER IDENTIFICATION. Proceedings - International Conference on Image Processing, ICIP, pp. 2891-2895. DOI: 10.1109/ICIP46576.2022.9898034 (click here)

(125.) Ombao, H., Pinto, M. (2022). Spectral Dependence. Econometrics and Statistics. DOI: 10.1016/j.ecosta.2022.10.005 (click here) PUBLICATION STAGE: Article in Press

(126.) Biswas, A., Ombao, H. (2022). FREQUENCY-SPECIFIC NON-LINEAR GRANGER CAUSALITY IN A NETWORK OF BRAIN SIGNALS. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2022-May, pp. 1401-1405. DOI: 10.1109/ICASSP43922.2022.9746794 (click here)

(127.) *Guerrero, M.B., Barreto-Souza, W., Ombao, H. (2022). Integer-valued autoregressive processes with prespecified marginal and innovation distributions: a novel perspective. Stochastic Models, 38 (1), pp. 70-90. DOI: 10.1080/15326349.2021.1977141 (click here)

PUBLISHED 2021

(101.) Chung, M.K., Ombao, H. (2021). Discussion of 'Event history and topological data analysis'. Biometrika, 108 (4), pp. 775-778. DOI: 10.1093/biomet/asab023 (click here)

(102.) Cruz, M., Pinto-Orellana, M.A., Gillen, D.L., Ombao, H.C. (2021). RITS: a toolbox for assessing complex interventions via interrupted time series models. BMC Medical Research Methodology, 21 (1), art. no. 143. DOI: 10.1186/s12874-021-01322-w (click here)

(103.) Pluta, D., Shen, T., Xue, G., Chen, C., Ombao, H., Yu, Z. (2021). Ridge-penalized adaptive Mantel test and its application in imaging genetics. Statistics in Medicine, 40 (24), pp. 5313-5332. DOI: 10.1002/sim.9127 (click here)

(104.) Raboudi, N.F., Ait-El-Fquih, B., Ombao, H., Hoteit, I. (2021). Ensemble Kalman filtering with coloured observation noise. Quarterly Journal of the Royal Meteorological Society, 147 (741), pp. 4408-4424. DOI: 10.1002/qj.4186 (click here)

(105.) Maia, G.D.O., Barreto-Souza, W., Bastos, F.D.S., Ombao, H. (2021). Semiparametric time series models driven by latent factor. International Journal of Forecasting, 37 (4), pp. 1463-1479. DOI: 10.1016/j.ijforecast.2020.12.007 (click here)

(106.) Chen, T., Sun, Y., Euan, C., Ombao, H. (2021). Clustering Brain Signals: a Robust Approach Using Functional Data Ranking. Journal of Classification, 38 (3), pp. 425-442. DOI: 10.1007/s00357-020-09382-1 (click here)

(107.) *Gao, X., Shen, W., Zhang, L., Hu, J., Fortin, N.J., Frostig, R.D., Ombao, H. (2021). Regularized matrix data clustering and its application to image analysis. Biometrics, 77 (3), pp. 890-902. DOI: 10.1111/biom.13354 (click here)

(108.) Chao, F., Guilmoto, C.Z., Ombao, H. (2021). Sex ratio at birth in Vietnam among six subnational regions during 1980-2050, estimation and probabilistic projection using a Bayesian hierarchical time series model with 2.9 million birth records. PLoS ONE, 16 (7 July), art. no. e0253721. DOI: 10.1371/journal.pone.0253721 (click here)

(109.) *Ting, C.-M., Samdin, S.B., Tang, M., Ombao, H. (2021). Detecting dynamic community structure in functional brain networks across individuals: A multilayer approach. IEEE Transactions on Medical Imaging, 40 (2), art. no. 9220100, pp. 468-480. DOI: 10.1109/TMI.2020.3030047 (click here)

(110.) Chung, M.K., Ombao, H. (2021). Lattice Paths for Persistent Diagrams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12929 LNCS, pp. 77-86. DOI: 10.1007/978-3-030-87444-5_8 (click here)

(111.) Jiao, S., Ombao, H. (2021). Shape-preserving prediction for stationary functional time series. Electronic Journal of Statistics, 15 (2), pp. 3996-4026. DOI: 10.1214/21-EJS1882 (click here)

(112.) Gorshkov, O., Ombao, H. (2021). Multi-chaotic analysis of inter-beat (R-R) intervals in cardiac signals for discrimination between normal and pathological classes. Entropy, 23 (1), art. no. 112, pp. 1-17. DOI: 10.3390/e23010112 (click here)

PUBLISHED 2020

(88.) Sundararajan, R.R., Frostig, R., Ombao, H. (2020). Modeling spectral properties in stationary processes of varying dimensions with applications to brain local field potential signals. Entropy, 22 (12), art. no. 1375, pp. 1-24. DOI: 10.3390/e22121375 (click here)

(89.) Fontaine, C., Frostig, R.D., Ombao, H. (2020). Modeling dependence via copula of functionals of Fourier coefficients. Test, 29 (4), pp. 1125-1144. DOI: 10.1007/s11749-020-00703-5 (click here)

(90.) Chao, F., Guilmoto, C.Z., Samir, K.C., Ombao, H. (2020). Probabilistic projection of the sex ratio at birth and missing female births by State and Union Territory in India. PLoS ONE, 15 (8 August), art. no. e0236673. DOI: 10.1371/journal.pone.0236673 (click here)

(91.) *Gao, X., Shen, W., Shahbaba, B., Fortin, N.J., Ombao, H. (2020). Evolutionary state-space model and its application to time-frequency analysis of local field potentials. Statistica Sinica, 30 (3), pp. 1561-1582. DOI: 10.5705/ss.202017.0420 (click here)  This paper won an award in the ENAR 2017 Student Paper Competition - This paper won an award in the JSM 2017 Student Paper Competition.

(92.) *Hu, L., Guindani, M., Fortin, N.J., Ombao, H. (2020). A hierarchical bayesian model for differential connectivity in multi-trial brain signals. Econometrics and Statistics, 15, pp. 117-135. DOI: 10.1016/j.ecosta.2020.03.009 (click here)

(93.) Fontaine, C., Frostig, R.D., Ombao, H. (2020). Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials. Econometrics and Statistics, 15, pp. 85-103. DOI: 10.1016/j.ecosta.2019.06.003 (click here)

(94.) Phang, C.-R., Noman, F., Hussain, H., Ting, C.-M., Ombao, H. (2020). A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia from EEG Connectivity Patterns. IEEE Journal of Biomedical and Health Informatics, 24 (5), art. no. 8836535, pp. 1333-1343. DOI: 10.1109/JBHI.2019.2941222 (click here)

(95.) Smith, R.J., Ombao, H.C., Shrey, D.W., Lopour, B.A. (2020). Inference on Long-Range Temporal Correlations in Human EEG Data. IEEE Journal of Biomedical and Health Informatics, 24 (4), art. no. 8819976, pp. 1070-1079. DOI: 10.1109/JBHI.2019.2936326 (click here)

(96.) Noman, F., Salleh, S.-H., Ting, C.-M., Samdin, S.B., Ombao, H., Hussain, H. (2020). A Markov-Switching Model Approach to Heart Sound Segmentation and Classification. IEEE Journal of Biomedical and Health Informatics, 24 (3), art. no. 8746548, pp. 705-716. DOI: 10.1109/JBHI.2019.2925036 (click here)

(97.) Huang, S.-G., Samdin, S.B., Ting, C.-M., Ombao, H., Chung, M.K. (2020). Statistical model for dynamically-changing correlation matrices with application to brain connectivity. Journal of Neuroscience Methods, 331, art. no. 108480. DOI: 10.1016/j.jneumeth.2019.108480 (click here)

(98.) Moraga, P., Ketcheson, D.I., Ombao, H.C., Duarte, C.M. (2020). Assessing the age- and gender-dependence of the severity and case fatality rates of COVID-19 disease in Spain.Wellcome Open Research, 5, art. no. 117. DOI: 10.12688/wellcomeopenres.15996.1 (click here)

(99.) Lan, S., Holbrook, A., Elias, G.A., Fortin, N.J., Ombao, H., Shahbaba, B. (2020). Flexible Bayesian Dynamic Modeling of Correlation and Covariance Matrices. Bayesian Analysis, 15 (4), pp. 1199-1228. DOI: 10.1214/19-BA1173 (click here)

(100.) *Ting, C.-M., Ombao, H., Salleh, S.-H., Latif, A.Z.A. (2020). Multi-Scale Factor Analysis of High-Dimensional Functional Connectivity in Brain Networks. IEEE Transactions on Network Science and Engineering, 7 (1), art. no. 8462768, pp. 449-465. DOI: 10.1109/TNSE.2018.2869862 (click here)

PUBLISHED 2019

(74.) *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.

(75.) *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

(76.)  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

(77.) *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.

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

(79.) 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

(80.) 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.

(81.) *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.

(82.) *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.

(83.) 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.

(84.) *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.

(85.) *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.

(86.) *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.

(87.) 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

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.

(73.) Shrey, D. W., McManus, O. K., Rajaraman, R., Ombao, H., Hussain, S. A., & Lopour, B. A. (2018). Strength and stability of EEG functional connectivity predict treatment response in infants with epileptic spasms. Clinical Neurophysiology, 129(10), 2137-2148.

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)