Masthead
NSF

E-prints

2017

  1. Bradley, J.R., Holan, S.H., and Wikle, C.K. (2017). Computationally Efficient Multivariate Spatio-Temporal Models for High-Dimensional Count-Valued Data. (With Discussion). To Appear - Bayesian Analysis.
  2. Bradley, J.R.,  Wikle, C.K., and  Holan, S.H. (2017). Regionalization of Multiscale Spatial Processes using a Criterion for Spatial Aggregation Error. Journal of the Royal Statistical Society -- Series B. 79, 815-832. 
  3. Holan, S.H., McElroy, T.S., and Wu, G. (2017). The Cepstral Model for Multivariate Time Series: The Vector Exponential Model, Statistica Sinica. 27, 23-42. 
  4. Linero, A.R., Bradley, J.R., and Desai, A . (2017). Multi-rubric Models for Ordinal Spatial Data with Application to Online Ratings from Yelp.
  5. Lucchesi, L.R., and Wikle, C.K., (2017). Visualizing uncertainty in areal data estimates with bivariate choropleth maps, map pixelation, and glyph rotation. STAT. 6, 292-302.
  6. McDermott, P.L., and Wikle, C.K. (2017). An ensemble quadratic echo state network for nonlinear spatio-temporal forecasting. STAT, in press.
  7. Schliep, E.M., A.E. Gelfand, J.S. Clark, B.J. Tomasek (2017). Biomass prediction using density dependent diameter distribution models. Annals of Applied Statistics. In press.
  8. Simpson, M., Wikle, C.K., and Holan, S.H. (2017). Adaptively-Tuned Particle Swarm Optimization with Application to Spatial Design.  STAT, 6, 145-159.
  9. Weinberg, D., et al. (16 co-authors, including Cressie, N., Holan, S.H., and Wikle, C.K.) (2017). Effects of a government-academic partnership: Has the NSF-Census Bureau Research Network helped secure the future of the Federal Statistical System? In preparation.
  10. Wu, G, Holan, S.H., Avril, A., and Waldström, J. (2017). A Bayesian Semiparametric Jolly-Seber Model with Individual Heterogeneity: An Application to Migratory Mallards at Stopover.  Submitted.
  11. Wu, G. and Holan, S.H. (2017). Bayesian Hierarchical Multi-Population Multistate Jolly-Seber Models with Covariates: Application to the Pallid Sturgeon Population Assessment Program, Journal of the American Statistical Association. 518, 471--483.

2016

  1. Bradley, J.R., Holan, S.H., and Wikle, C.K. (2016). Bayesian Hierarchical Models with Conjugate Full-Conditional Distributions for Dependent Data from the Natural Exponential Family. Under Invited Revision - Journal of the American Statistical Association - T&M.
  2. Bradley, J.R., Wikle, C.K., and Holan, S.H. (2016). Bayesian Spatial Change of Support for Count-Valued Survey Data with Application to the American Community Survey. Journal of the American Statistical Association. 111, 472-487. 
  3. Bradley, J.R., Wikle, C.K., and Holan, S.H. (2016). Hierarchical Models for Spatial Data with Errors that are Correlated with the Latent Process. Under Invited Revision -Statistica Sinica.
  4. Bradley, J.R., Cressie, N., and Shi, T. (2016). A Comparison of Spatial Predictors when Datasets Could be Very Large. Statistics Surveys, 10, 100-131.
  5. 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.
  6. Cressie, N. and Zammit-Mangion, A. (2016) Multivariate Spatial Covariance Models: A Conditional Approach. Biometrika, 103.4, 915-935.
  7. Holan, S.H. and Wikle, C.K. (2016). Hierarchical Dynamic Generalized Linear Mixed Models for Discrete--Valued Spatio-Temporal Data. Handbook of Discrete--Valued Time Series. Richard A. Davis, Scott H. Holan, Robert Lund, and Nalini Ravishanker (Eds.). 327--348, Chapman & Hall/CRC.
  8. Lund, R., Holan, S.H., and Livsey, J. (2016). Long Memory Discrete--Valued Time Series. Handbook of Discrete--Valued Time Series, Richard A. Davis, Scott H. Holan, Robert Lund, and Nalini Ravishanker (Eds.). 447-458, Chapman & Hall/CRC.
  9. 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.
  10. Quick, H., Holan, S.H., and Wikle, C.K. (2016). Generating Partially Synthetic Geocoded Public Use Data with Decreased Disclosure Risk Using Differential Smoothing.  Under Invited Revision -- Journal of the Royal Statistical Society - Series A.
  11. Yang, W.H.,  Holan, S.H., and Wikle, C.K. (2016). Bayesian Lattice Filters for Time-Varying Autoregression and Time-Frequency Analysis. Bayesian Analysis. 11, 977-1003.

2015

  1. Cressie, N. and Burden, S. (2015). Evaluation of diagnostics for hierarchical spatial statistical models. Geometry Driven Statistics. Wiley. Chichester, UK. 241-259.
  2. Cressie, N. and Chambers, R.L. (2015). Comment: Spatial sampling designs depend as much on "how much?" and "why?" as on "where?" [Comment on "Optimal design in geostatistics under preferential sampling" by G. da Silva Ferreira and D. Gamerman.] Bayesian Analysis. 10, 741-748.
  3. Cressie, N. and Zammit-Mangion, A. (2015). Multivariate Spatial Covariance Models: A Conditional Approach. arXiv preprint: 1504.01865.
  4. Cressie, N. and Burden, S. (2015). Figures of Merit for Simultaneous Inference and Comparisons in Simulation Experiments. STAT, 1: 196 - 211.
  5. 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.)
  6. 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.
  7. Bradley, J.R., Wikle, C.K., and Holan, S.H. (2015) Multiscale Analysis of Survey Data: Recent Developments and Exciting Prospects. Statistic Views, Wiley.
  8. 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 Estimates. STAT. 4, 255 – 270.
  9. Burden, S., Cressie, N., and Steel, D.G. (2015) The SAR Model for Very Large Datasets: A Reduced Rank Approach. Econometrics. 3, 317 – 338.
  10. Cressie, N. and Chambers, R.L. (2015) Comment on Article by Ferreira and Gammerman. Bayesian Analysis. 10, 741 – 748.
  11. 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.
  12. Porter, A.T., Wikle, C.K., and Holan, S.H. (2015) Small Area Estimation via Multivariate Fay-Herriot Models with Latent Spatial Dependence. Australian & New Zealand Journal of Statistics. 57, 15 – 29.
  13. 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.
  14. 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.
  15. 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 Mortality. STAT, 4, 227 – 234.
  16. 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.
  17. 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 Model. Proceedings of The National Conference On Undergraduate Research (NCUR). Eastern Washington University, Cheney, WA.

2014

  1. McElroy, T.S. and Holan, S.H. (2014) Asymptotic Theory of Cepstral Random Fields. Annals of Statistics. 4, 64 – 86.
  2. McElroy, T.S. and Holan, S.H. (2014) Fast Estimation of Time Series with Multiple Long-Range Persistencies, ASA Proceedings of the Joint Statistical Meetings, American Statistical Association (Alexandria, VA).
  3. Porter, A.T., Holan, S.H., Wikle, C.K., and Cressie, N. (2014) Spatial Fay-Herriot Models for Small Area Estimation with Functional Covariates. Spatial Statistics. 10, 27 – 42.
  4. Porter, A.T. and Oleson, J. (2014) A CAR Model for Multiple Outcomes on Mismatched Lattices. Spatial and Spatio-Temporal Epidemiology. 11, 79 – 88.
  5. Wikle, C.K. (2014) Agent Based Models: Statistical Challenges and Opportunities. Statistic Views, Wiley.
  6. Zhuang, L. and Cressie, N. (2014) Bayesian Hierarchical Statistical SIRS Models. Statistical Methods and Applications, 23, 601 – 646.

2013

  1. Holan, S.H. and Wikle, C.K. (2013) Semiparametric Dynamic Design of Monitoring Networks for Non-Gaussian Spatio-Temporal Data. Spatio-temporal Design: Advances in Efficient Data Acquisition, Jorge Mateu and Werner Muller (Eds.), 269 - 284, Wiley, Chichester, UK.
  2. Sengupta, A., and Cressie, N. (2013) Hierarchical Statistical Modeling of Big Spatial Datasets Using the Exponential Family of Distributions. Spatial Statistics. 4, 14 - 44.
  3. 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.
  4. Yang, W.H., Wikle, C.K., Holan, S.H., and Wildhaber, M.L. (2013) Ecological Prediction with Nonlinear Multivariate Time-Frequency Functional Data Models. Journal of Agricultural, Biological, and Environmental Statistics. 18, 450 – 474.

2012

  1. 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.
  2. Wang, J. and Holan, S.H. (2012) Bayesian Multi-Regime Smooth Transition Regression with Ordered Categorical Variables. Computational Statistics and Data Analysis. 56, 4165 – 4179.