Masthead
NSF

E-prints

  1. Bradley, J.R., Holan, S.H., and Wikle, C.K. (2016). Multivariate Spatio-Temporal Survey Fusion with Application to the American Community Survey and Local Area Unemployment Statistics. STAT, 5: 224 - 233.
  2. McElroy, T.S. and Holan, S.H. (2016). Computation of the Autocovariances for Time Series with Multiple Long-Range Persistencies. Computational Statistics and Data Analysis, 101: 44 - 56.
  3. Quick, H., Holan, S.H., and Wikle, C.K. (2016). Generating Partially Synthetic Geocoded Public Use Data with Decreased Disclosure Risk Using Differential Smoothing. arXiv preprint: 1507.05529.
  4. Bradley, J.R., Wikle, C.K., and Holan, S.H. (2016 – To Appear) Bayesian Spatial Change of Support for Count-Valued Survey DataJournal of the American Statistical Association.
  5. Bradley, J.R., Wikle, C.K., and Holan, S.H. (2016 – To Appear) Regionalization of Multiscale Spatial Processes using a Criterion for Spatial Aggregation Error. Journal of the Royal Statistical Society: Series B.
  6. Wu, G. and Holan, S.H. (2015). Bayesian Hierarchical Multi-Population Multistate Jolly-Seber Models with Covariates: Application to the Pallid Sturgeon Population Assessment Program. (To Appear - Journal of the American Statistical Association).
  7. Quick, H., Holan, S.H., Wikle, C.K., and Reiter, J.P. (2015). Bayesian Marked Point Process Modeling for Generating Fully Synthetic Public Use Data with Point-Referenced Geography. Spatial Statistics, 14: 439 - 451.
  8. Cressie, N. and Zammit-Mangion, A. (2015). Multivariate Spatial Covariance Models: A Conditional Approach. arXiv preprint: 1504.01865.
  9. Cressie, N. and Burden, S. (2015). Figures of Merit for Simultaneous Inference and Comparisons in Simulation Experiments. STAT, 1: 196 - 211.
  10. Bradley, J.R., Cressie, N., and Shi, T. (2015). Comparing and Selecting Spatial Predictors Using Local Criteria (with discussion). Test, 24: 1-28. (Rejoinder: 2015, Vol. 24, pp. 54-60.)
  11. Bradley, J.R., Holan, S.H., and Wikle, C.K. (2015). Computationally Efficient Multivariate Spatio-Temporal Models for High-Dimensional Count-Valued Data. arXiv preprint: 1512.07273.
  12. Bradley, J.R., Holan, S.H., and Wikle, C.K. (2015)  Multivariate Spatio-Temporal Models for High-Dimensional Areal Data with Application to Longitudinal Employer-Household Dynamics. Annals of Applied Statistics. 9, 1761 – 1791.
  13. Bradley, J.R., Wikle, C.K., and Holan, S.H. (2015) Multiscale Analysis of Survey Data: Recent Developments and Exciting Prospects. Statistic Views, Wiley.
  14. Bradley, J.R., Wikle, C.K., and Holan, S.H. (2015) Spatio-Temporal Change of Support with Application to American Community Survey Multi-Year Period EstimatesSTAT. 4, 255 – 270.
  15. Burden, S., Cressie, N., and Steel, D.G. (2015) The SAR Model for Very Large Datasets: A Reduced Rank Approach. Econometrics. 3, 317 – 338.
  16. Cressie, N. and Chambers, R.L. (2015) Comment on Article by Ferreira and Gammerman. Bayesian Analysis. 10, 741 – 748.
  17. Cressie, N. and Zammit-Mangion, A. (2015) Multivariate Spatial Covariance Models: A Conditional ApproacharXiv preprint: 1504.01865.
  18. Holan, S.H., McElroy, T.S., and Wu, G. (2015 – To Appear) The Cepstral Model for Multivariate Time Series: The Vector Exponential ModelStatistica Sinica.
  19. Porter, A.T., Holan, S.H., and Wikle, C.K. (2015) Bayesian Semiparametric Hierarchical Empirical Likelihood Spatial Models Journal of Statistical Planning and Inference, 165, 78 – 90.
  20. Porter, A.T., Wikle, C.K., and Holan, S.H. (2015) Small Area Estimation via Multivariate Fay-Herriot Models with Latent Spatial DependenceAustralian & New Zealand Journal of Statistics. 57, 15 – 29.
  21. Porter, A.T., Holan, S.H., and Wikle, C.K. (2015) Multivariate Spatial Hierarchical Bayesian Empirical Likelihood Methods for Small Area Estimation. STAT, 4: 108 – 116.
  22. Quick, H, Holan, S.H., and Wikle, C.K. (2015) Zeros and Ones: A Case for Suppressing Zeros in Sensitive Count Data with an Application to Stroke MortalitySTAT, 4, 227 – 234.
  23. Quick, H., Holan, S.H., Wikle, C.K., and Reiter, J.P. (2015) Bayesian Marked Point Process Modeling for Generating Fully synthetic Public Use Data with Point-Referenced GeographySpatial Statistics, 14, 439 – 451.
  24. Ryan, M, Bradley, J.R., Oswald, T, Wikle, C.K., and Holan, S.H. (2015) An Analysis of Bullying and Suicide in the United States using a Non-Gaussian Multivariate Spatial ModelProceedings of The National Conference On Undergraduate Research (NCUR). Eastern Washington University, Cheney, WA.
  25. Yang, W.H., Holan, S.H., and Wikle, C.K. (2015 – To Appear) Bayesian Lattice Filters for Time-Varying Autoregression and Time-Frequency AnalysisBayesian Analysis.
  26. Bradley, J.R., Cressie, N., and Shi, T. (2015). A Comparison of Spatial Predictors when Datasets Could be Very Large (To Appear - Statistics Surveys).
  27. McElroy, T.S. and Holan, S.H. (2014) Asymptotic Theory of Cepstral Random FieldsAnnals of Statistics. 4, 64 – 86.
  28. McElroy, T.S. and Holan, S.H. (2014) Fast Estimation of Time Series with Multiple Long-Range PersistenciesASA Proceedings of the Joint Statistical Meetings, American Statistical Association (Alexandria, VA).
  29. Porter, A.T., Holan, S.H., Wikle, C.K., and Cressie, N. (2014) Spatial Fay-Herriot Models for Small Area Estimation with Functional CovariatesSpatial Statistics. 10, 27 – 42.
  30. Porter, A.T. and Oleson, J. (2014) A CAR Model for Multiple Outcomes on Mismatched LatticesSpatial and Spatio-Temporal Epidemiology. 11, 79 – 88.
  31. Wikle, C.K. (2014) Agent Based Models: Statistical Challenges and OpportunitiesStatistic Views, Wiley.
  32. Zhuang, L. and Cressie, N. (2014) Bayesian Hierarchical Statistical SIRS ModelsStatistical Methods and Applications, 23, 601 – 646.
  33. Holan, S.H. and Wikle, C.K. (2013) Semiparametric Dynamic Design of Monitoring Networks for Non-Gaussian Spatio-Temporal DataSpatio-temporal Design: Advances in Efficient Data Acquisition, Jorge Mateu and Werner Muller (Eds.), 269 - 284, Wiley, Chichester, UK.
  34. Sengupta, A., and Cressie, N. (2013) Hierarchical Statistical Modeling of Big Spatial Datasets Using the Exponential Family of Distributions. Spatial Statistics. 4, 14 - 44.
  35. Yang, W.H., Wikle, C.K., Holan, S.H., and Wildhaber, M.L. (2013) Ecological Prediction with Nonlinear Multivariate Time-Frequency Functional Data ModelsJournal of Agricultural, Biological, and Environmental Statistics. 18, 450 – 474.
  36. Wu, G., Holan, S.H., and Wikle, C.K. (2013) Hierarchical Bayesian Spatio-Temporal Conway-Maxwell Poisson Models with Dynamic Dispersion. Journal of Agricultural, Biological, and Environmental Statistics. 18, 335 – 356.
  37. Holan, S.H., Yang, W.H., Matteson, D.S., and Wikle, C.K. (2012) An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models. (With Discussion) Applied Stochastic Models in Business and Industry. 28, 485 – 499.
  38. Wang, J. and Holan, S.H. (2012) Bayesian Multi-Regime Smooth Transition Regression with Ordered Categorical VariablesComputational Statistics and Data Analysis. 56, 4165 – 4179.