Publications 2010 to 2014

 

* denotes PhD Student​

[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.​

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