Peter F. Craigmile

Professor, Department of Statistics, The Ohio State University

Email: pfc <at> stat.osu.edu
Office: Cockins Hall, Room 427

Publications

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  1. P. F. Craigmile and D. Mondal, Estimation of long-range dependence in gappy Gaussian time series. Statistics and Computing, DOI: 10.1007/s11222-019-09874-0.
    [Supplemental material] [R code]
  2. S. Tang, P. F. Craigmile, and Y. Zhu, Spectral estimation using multitaper Whittle methods with a lasso penalty. IEEE Transactions on Signal Processing. 67, 4992-5003.
  3. C. Adibe, P. F. Craigmile, N. Onnen, E. Schwartz, and M. E. Roberts (2019), The Relationship Between Tobacco Retailer Density and Neighborhood Demographics in Ohio, Ohio Journal of Public Health, 4.
    [Supplemental material]
  4. D. Kunkel, K. Potter, P. F. Craigmile, M. Peruggia, T. Van Zandt (2019), A Bayesian race model for response times under cyclic stimulus discriminability, Annals of Applied Statistics. 13, 271-296.
  5. P. F. Craigmile, Time series methodology (2019), A chapter in A. Gelfand, M. Fuentes, J. Hoeting, and R. Smith (Eds.): Handbook of Environmental and Ecological Statistics, Chapman and Hall/CRC, New York: NY (peer-reviewed).
  6. P. F. Craigmile and P. Guttorp, Modeling and assessing climatic trends (2019), A chapter in A. Gelfand, M. Fuentes, J. Hoeting, and R. Smith (Eds.): Handbook of Environmental and Ecological Statistics, Chapman and Hall/CRC, New York: NY (peer-reviewed).
    [R code]
  7. P. F. Craigmile, M. Haran, B. Li, E. Mannshardt, B. Rajaratnam, and M. Tingley, Paleoclimate reconstruction: looking backwards to look forward (2019), A chapter in A. Gelfand, M. Fuentes, J. Hoeting, and R. Smith (Eds.): Handbook of Environmental and Ecological Statistics, Chapman and Hall/CRC, New York: NY (peer-reviewed).
  8. J. Yin, P. F. Craigmile, X. Xu, and S. MacEachern (2019), Shape-Constrained semiparametric additive stochastic volatility models. Statistical Theory and Related Fields, 3, 71-82.
  9. M. L. Burgoon, T. Albani, B. Keller-Hamilton, B. Lu, M. E. Roberts, P. F. Craigmile, C. Browning, W. Xi, and A. K. Ferketich (2019), Exposures to the Tobacco Retail Environment among Adolescent Boys in Urban and Rural Environments. The American Journal of Drug and Alcohol Abuse, 45, 217-226.
  10. J. Yin, and P. F. Craigmile (2018), Heteroscedastic asymmetric spatial processes (HASP). Stat, 7, e206
    [Supplemental material].
  11. P. F. Craigmile (2017), Editorial: The Role of Statistics in Climate Research, Chance, 30, 4-5.
  12. P. F. Craigmile and B. Li (2017), Instruments, Proxies, and Simulations: exploring imperfect measures of climate. Chance, 30, 12-18.
  13. S. Kim, K. Potter, P. F. Craigmile, M. Peruggia, T. Van Zandt (2017), A Bayesian Race Model for Recognition Memory, Journal of the American Statistical Association, 112, 77-91
    [Supplemental material including data and code].
  14. P. F. Craigmile and D. B. Percival (2017). Discussion of Guy Nason, Ben Powell, Duncan Elliott and Paul A Smith, Should we sample a time series more frequently? Decision support via multirate spectrum estimation. Journal of the Royal Statistical Society: Series A, DOI: 10.1111/rssa.12210.
  15. G. Schneider, P. F. Craigmile and R. Herbei (2017), Maximum Likelihood Estimation for Stochastic Differential Equations Using Sequential Gaussian-Process-Based Optimization. Technometrics, 59, 178-188.
  16. P. F. Craigmile (2016). Discussion of by Ilvonen et al.: A Bayesian multinomial regression model for paleoclimate reconstruction with time uncertainty, Environmetrics, 27, 423-424.
  17. P. F. Craigmile (2016). Book review of Steven P. Millard, EnvStats: An R Package for Environmental Statistics. Journal of Agricultural, Biological, and Environmental Statistics, 22, 107-108.
  18. M. Tingley, P. F. Craigmile, M. Haran, B. Li, E. Mannshardt, and B. Rajaratnam (2015), On discriminating between GCM forcing configurations using Bayesian reconstructions of Late-Holocene temperatures. Journal of Climate, 28, 8264-8281.
  19. P. F. Craigmile, P. Guttorp, R. Lund, R. L. Smith, P. W. Thorne, and D. Arndt (2014), Warm Streaks in the US Temperature Record: What are the Chances?". The Journal of Geophysical Research, 119, 5757-5766.
    [Supplemental material including data and code]
  20. P. F. Craigmile and P. Guttorp (2013). Can a regional climate model reproduce observed extreme temperatures?. Statistica, 73, 103-122.
  21. P. F. Craigmile, M. P. Tingley, and J. Yin (2013). Paleoclimate reconstruction using statistical non-linear forward models. Invited Paper, Proceedings of the 59th ISI World Statistics Congress, Hong Kong (not peer reviewed)
  22. R. W. Katz, P. F. Craigmile, P. Guttorp, M. Haran, B. Sanso, and M. L. Stein (2013). Uncertainty analysis in climate change assessments. Nature Climate Change, 3, 769-771 (Editor reviewed).
  23. E. Mannshardt, P. F. Craigmile, and M. P. Tingley (2013). Statistical modeling of extreme value behavior in North American tree-ring density series. Climatic Change, 117, 843-858.
    [Supplemental material]
  24. C. Chanialidis, P. Craigmile, V. Davies, N. Dean, L. Evers, M. Filiippone, M. Gupta, S. Ray and S. Rogers (2013). Discussion of Henning and Liao: How to find an appropriate clustering for mixed type variables with application to socio-economic stratification. Journal of the Royal Statistical Society: Series C. 62, 309-369.
  25. L. Wei, P. F. Craigmile, and W. M. King (2012).
    Spectral-based noncentral F mixed effect models, with application to otoacoustic emissions. Journal of Time Series Analysis, 33, 850-862.
    [Supplemental material] [R code]
  26. V. J. Berrocal, P. F. Craigmile, and P. Guttorp (2012). Regional climate model assessment using statistical upscaling and downscaling techniques. Environmetrics, 23, 482-492.
    [Supplemental material] [Supplemental quarterly differences (MOV)]
  27. P. F. Craigmile, M. Peruggia, and T. Van Zandt (2012). A Bayesian Hierarchical Model for Response Time Data Providing Evidence for Criteria Changes Over Time. A chapter in: M. C. Edwards and R. C. MacCallum (Eds.). Current Issues in the Theory and Application of Latent Variable Models. New York, NY: Taylor and Francis (peer reviewed).
  28. C. Hans, G. M. Allenby, P. F. Craigmile, J. H. Lee, S. MacEachern, and X. Xu (2012) Covariance Decompositions for Accurate Computation in Bayesian Scale-Usage Models. Journal of Computational and Graphical Statistics, 21, 538-557.
    [Supplemental material]
  29. M. Tingley, P. F. Craigmile, M. Haran, B. Li, E. Mannshardt, and B. Rajaratnam (2012). Piecing together the past: Statistical insights into paleoclimatic reconstructions. Quaternary Science Reviews, 35, 1-22.
  30. P. F. Craigmile and P. Guttorp (2011). Space-time modeling of trends in temperature series. Journal of Time Series Analysis, 32, 378-395.
    [Supplemental material]
  31. P. Craigmile and B. Rajaratnam (2011). Discussion of McShane And Wyner: A Statistical Analysis of Multiple Temperature Proxies: Are Reconstruction of Surface Temperature Over the Last 1000 Years Reliable?. Annals of Applied Statistics, 5, 88-90.
  32. P. F. Craigmile, M. Peruggia, and T. Van Zandt (2010). Hierarchical Bayes Models for Response Time Data. Psychometrika, 75, 613-632.
  33. L. Wei and P. F. Craigmile (2010). Global and local spectral-based tests for periodicities. Biometrika, 97, 223-230.
    [R package]
  34. P. F. Craigmile, C. A. Calder, R. Paul, H. Li, and N. Cressie (2009). Hierarchical Model Building, Fitting, and Checking: A Behind-the-Scenes Look at a Bayesian Analysis of Arsenic Exposure Pathways. Bayesian Analysis, 4, 1-35 (with Discussion 37-62).
    [Supplemental material]
  35. P. F. Craigmile, M. Peruggia, and T. Van Zandt (2009). Detrending Response Time Series. A chapter in S. M. Chow, E. Ferrer, and F. Hsieh (Eds.). Statistical methods for modeling human dynamics: An interdisciplinary dialogue. Notre Dame Series on Quantitative Methodology (Vol. 4). New York, NY: Taylor and Francis.
    [R code]
  36. P. F. Craigmile (2009). Some interesting facets of spectral analysis. The ISBA Bulletin, 16 (3). 8-11.
  37. C. A. Calder, P. F. Craigmile, and J. Zhang (2009). Regional Spatial Modeling of Topsoil Geochemistry. Biometrics, 65, 206-215.
    [R code]
  38. T. J. Santner, P. F. Craigmile, C. A. Calder, and R. Paul (2008). Demographic and Behavioral Modifiers of Arsenic Exposure Pathways: A Bayesian Hierarchical Analysis of NHEXAS Data. Environmental Science & Technology, 42, 5607-5614.
    [Supplemental material]
  39. J. Zhang, P. F. Craigmile, and N. Cressie (2008). Loss Function Approaches to Predict a Spatial Quantile and Its Exceedance Region. Technometrics, 50 (2). 216-227.
  40. C. A. Calder, P. F. Craigmile, and E. Mosley-Thompson (2008). Spatial Variation in the Influence of the North Atlantic Oscillation on Precipitation Across Greenland. Journal of Geophysical Research-Atmospheres, 113, D06112.
  41. R. Paul, N. Cressie, B. E. Buxton, C. A. Calder, P. F. Craigmile, H. Li, N. J. McMillan, M. Morara, J. Sanford, T. J. Santner, and J. Zhang (2007). Bayesian Hierarchical Model of Arsenic Exposure Based on NHEXAS Data: A Comparison of US EPA Region 5 and Arizona. Proceedings of the 2007 Joint Statistical Meetings, 1055-1062 (not peer reviewed)
  42. N. Cressie, B. Buxton, C. A. Calder, P. F. Craigmile, C. Dong, N. McMillan, M. Morara, T. J. Santner, K.Wang, G. Young, and J. Zhang (2007). From Sources to Biomarkers: A Hierarchical Bayesian Approach for Human Exposure Modeling. Journal of Statistical Planning and Inference, 137, 3361-3379
  43. P. F. Craigmile, N. Kim, S. Fernandez, and B. Bonsu (2007). Modeling and detection of respiratory-related outbreak signatures. BMC Medical Informatics and Decision Making, 7:28.
  44. N. Cressie, J. Zhang, and P. F. Craigmile (2005). Geostatistical prediction of spatial extremes and their extent. Geostatistics for Environmental Applications, Proceedings of the Fifth European Conference on Geostatistics for Environmental Applications, edited by P. Renard, H. Demougeot-Renard, R. Froidevaux, Springer, 27-37 (peer reviewed)
  45. E. Mosley-Thompson, C. R. Readinger, P. Craigmile, L. G. Thompson, and C. A. Calder (2005). Regional Sensitivity of Greenland Precipitation to NAO Variability. Geophysical Research Letters, 32, L24707.
  46. B. Whitcher, P. F. Craigmile, and P. Brown (2005). Time-varying Spectral Analysis in Neurophysiological Time Series Using Hilbert Wavelet Pairs. Signal Processing, 85, 2065-2081.
    [R package]
  47. P. F. Craigmile, (2005). A book review of L. Wasserman (2004). All of statistics: a concise course in statistical inference, Springer-Verlag, New York. The American Statistician, 59, 203-204.
  48. P. F. Craigmile, N. Cressie, T. J. Santner, and Y. Rao (2005). A loss function approach to identifying environmental exceedances. Extremes, 8, 143-159.
  49. P. F. Craigmile, P. Guttorp, and D. B. Percival (2005). Wavelet based estimation for polynomial contaminated fractionally differenced processes. IEEE Transactions on Signal Processing, 53, 3151-3161
    [Technical appendix]
  50. P. F. Craigmile (2005). Approximate wavelet based simulation of long memory processes. Journal of Statistical Computation and Simulation, 75, 363-80
  51. P. F. Craigmile, and D. B. Percival (2005). Asymptotic decorrelation of between-scale wavelet coefficients. IEEE Transactions on Information Theory, 51, 1039-1048
  52. N. Cressie, J. Zhang, and P. F. Craigmile (2005). Geostatistical prediction of spatial extremes and their extent. Geostatistics for Environmental Applications, Proceedings of the Fifth European Conference on Geostatistics for Environmental Applications, edited by P. Renard, H. Demougeot-Renard, R. Froidevaux, Springer, 27-37 (peer reviewed)
  53. N. Altman, D. Banks, J. Hardwick, K. Roeder, P. Craigmile, J. Hardin, and M. Gupta (2005). The Institute of Mathematical Statistics New Researchers' Survival Guide.
  54. B. Whitcher, and P. F. Craigmile (2004). Multivariate spectral analysis using Hilbert wavelet pairs. International Journal of Wavelets, Multiresolution and Information Processing. 2, 567-587
    [R package]
  55. P. F. Craigmile, and W. M. King (2004). Periodogram based tests for distortion products otoacoustic emission. The Journal of the Acoustical Society of America, 116, 442-451
    [R package]
  56. P. F. Craigmile, P. Guttorp, and D. B. Percival (2004). Trend assessment in a long memory dependence model using the discrete wavelet transform. Environmetrics, 15, 313-335.
  57. P. F. Craigmile (2003). Simulating a class of stationary Gaussian processes using the Davies-Harte algorithm, with application to long memory processes. Journal of Time Series Analysis, 24, 505-511.
    [R code]
  58. P. F. Craigmile, and D. B. Percival (2002). Wavelet-based trend detection and estimation. Entry in the Encyclopedia of Environmetrics, edited by A. El-Shaarawi and W. W. Piegorsch, Chichester, UK: John Wiley & Sons (peer reviewed)
  59. P. F. Craigmile, D. B. Percival, and P. Guttorp (2001). The impact of wavelet coefficient correlations on fractionally differenced process estimation. European Congress of Mathematics (Barcelona, Joly 10-14, 2000). Volume II, edited by C. Casacuberta, R. M. Miro-Roig, J. Verdera and S. Xambo-Descamps, Basel, Switzerland: Birkhauser Verlag, 591-599 (not peer reviewed)
  60. P. F. Craigmile (2000). Wavelet-based estimation for trend contaminated long memory processes. Ph.D. Dissertation, University of Washington.
  61. P. F. Craigmile, and D. M. Titterington (1997). Parameter estimation for finite mixtures of uniform distributions. Communications in Statistics - Theory and Methods, 26, 1981-1995.

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