Ori Rosen

Professor

Department of Mathematical Sciences
University of Texas at El Paso
Bell Hall 221
El Paso, TX 79968
E-mail:orosen@utep.edu
Telephone: (915) 747-6843

Research Interests

Bayesian computational methods (MCMC), Ecological inference, Mixtures-of-experts, Time series, Functional data analysis.

Selected Publications

• Rosen, O. and Tanner, M. (1999). Mixtures of Proportional Hazards Regression Models, Statistics in Medicine, 18, 1119-1131.
• King, G., Rosen, O. and Tanner, M. (1999). Binomial-Beta Hierarchical Models for Ecological Inference, Sociological Methods and Research, 28, 61-90 (special issue on Bayesian methods in the social sciences)
• Rosen, O., Jiang, W. and Tanner, M. (2000). Mixtures of Marginal Models. Biometrika, 87, 391-404.
• Rosen, O., Jiang, W., King, G. and Tanner, M.A. (2001). Bayesian and Frequentist Inference for Ecological Inference: the R x C Case, Statistica Neerlandica, 55, 134-156 (special issue on analysis of repeated cross-sectional data)
• Liao, J. and Rosen, O. (2001). Fast and Stable Algorithms for Computing and Sampling from the Noncentral Hypergeometric Distribution, The American Statistician, 55, 366-369.
• Rosen, O. and Cohen, A. (2003). Analysis of Growth Curves via Mixtures, Statistics in Medicine, 22, 3641-3654.
• King, G., Rosen, O. and Tanner, M.A. (eds.) Ecological Inference: New Methodological Strategies, Cambridge University Press (2004).
• Rosen, O. and Stoffer, D.S. (2007). Automatic Estimation of Multivariate Spectra via Smoothing Splines. Biometrika, 94, 335-345.
• Sun, Z., Rosen, O. and Sampson, A.R. (2007). Multivariate Bernoulli Mixture Models with Application to Postmortem Tissue Studies in Schizophrenia. Biometrics, 63, 901-909.
• Thompson, W. and Rosen, O. (2008). A Bayesian Model for Sparse Functional Data. Biometrics, 64, 54-63.
• King, G., Rosen, O. and Tanner, M.A. Ecological inference. To appear in The New Palgrave Dictionary of Economics, Second Edition
• King, G., Rosen, O., Tanner, M.A. and Wagner, A.F. (2008). Ordinary Economic Voting Behavior in the Extraordinary Election of Adolf Hitler. Journal of Economic History, 68, 951-996.
• Rosen, O., Stoffer, D. and Wood, S. (2009). Local Spectral Analysis via a Bayesian Mixture of Smoothing Splines. J. of the American Statistical Association, 104, 249-262. Fortran program
• Rosen, O. and Thompson, W. (2009). A Bayesian Regression Model for Multivariate Functional Data. J. of Computational Statistics and Data Analysis, 53, 3773-3786. Matlab Programs
• Wood, S., Rosen, O. and Kohn, R. (2011). Bayesian Mixtures of Autoregressive Models . (Appendices) . J. of Computational and Graphical Statistics, 20, 174-195.
• Rosen, O., Wood, S. and Stoffer, D. (2012). AdaptSPEC: Adaptive Spectral Estimation for Nonstationary Time Series. J. of the American Statistical Association, 107, 1575-1589. Matlab Programs, R package
• Rosen, O. and Thompson, W. (2015). Bayesian Semiparametric Copula Estimation with Application to Psychiatric Genetics, Biometrical Journal, 57, 468-484.
• Krafty, R. T., Rosen, O., Stoffer, D. S., Buysse, D. J. and Hall, M. (2017). Conditional Spectral Analysis of Replicated Multiple Time Series with Application to Nocturnal Physiology, J. of the American Statistical Association, 112, 1405-1416. Matlab code
• Bertolacci, M., Cripps, E., Rosen, O., Lau, J. and Cripps, S. (2019). Climate Inference on Daily Rainfall Across the Australian Continent, 1876-2015. Annals of Applied Statistics, 13, 683-712. pdf
• Marchant, R., Samia, N.I., Rosen, O., Tanner, M.A. and Cripps, S. (2020). Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions
• Li, Z., Rosen, O., Ferrarelli, F. and Krafty, R.T. (2021). Adaptive Bayesian Spectral Analysis of High-Dimensional Nonstationary Time Series, J. of Computational and Graphical Statistics, 30, 794-807.
• Bertolacci, M., Rosen, O., Cripps, E. and Cripps, S. (2022). AdaptSPEC-X: Covariate Dependent Spectral Modeling of Multiple Nonstationary Time Series , J. of Computational and Graphical Statistics, 31, 436-454.

Grants as PI

• NIH 2018-2021, $112.15K abstract
• NSF DMS, 2015-2018, $250K abstract
• NSA, 2012-2014, $62K
• NSF DMS, 2008-2011, $120K abstract
• NSF DMS, 2007-2008, $19K abstract

Courses Taught

• Fall 2005: Mathematical Statistics I (5380)
• Spring 2006:
  1. Mathematical Statistics II (5381)
  2. Graduate Seminar (5195)
  3. Statistical Methods I (2380) (Course material on webct)
• Fall 2006:
  1. Statistics I (4380)
  2. Graduate Seminar (5195)
  3. Statistics in Research (5385)
• Spring 2007:
  1. Statistical Methods I (2380)
  2. Graduate Seminar (5195)
  3. Statistical Computing (5392)
• Fall 2007:
  1. Statistics I (4380)
  2. Statistics in Research (5385)
  3. Lab for 5385 (5195)
• Spring 2008:
  1. Probability (3330)
  2. Categorical Data Analysis (5336)
  3. Lab for 2380 (2182)
• Fall 2008:
  1. Statistics I (4380)
  2. Time Series (5391)
  3. Graduate Seminar (5195)
• Spring 2009:
  1. Lab for 2380 (2182)
  2. Probability (3330)
  3. Statistical Computing (5392)
• Fall 2009:
  1. Labs for 2480
  2. Applied Regression Analysis (4385)
  3. Mathematical Statistics I (5380)
• Spring 2010:
  1. Mathematical Statistics I (5381)
  2. Statistical Computing (5392)
• Fall 2010:
  1. Elementary Statistical Methods (2480)
  2. Statistics in Research (5385)
• Spring 2011:
  1. Elementary Statistical Methods (2480)
  2. Mathematical Statistics I (5381)
• Fall 2011:
  1. Applied Regression Analysis (4385)
  2. Categorical Data Analysis (5336)
  3. Graduate Seminar (5195)
• Spring 2012:
  1. Statistics I (4380)
  2. Time Series (5391)
  3. Lab for Stat 2480
• Fall 2012:
  1. Statistics in Research (5385)
  2. Statistical Computing (5392)
• Spring 2013:
  1. Statistics I (4380)
  2. Mathematical Statistics II (5381)
• Fall 2013:
  1. Applied Regression Analysis (4385)
  2. Categorical Data Analysis
• Fall 2014:
  1. Applied Regression Analysis (4385)
  2. Graduate Seminar (5195)
  3. Statistical Computing (5392)
• Spring 2015:
  1. Elementary Statistical Methods (2480)
  2. Time Series (5391)