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Harrison Quick, PhD

Harrison Quick

Assistant Professor
Epidemiology and Biostatistics
hsq23@drexel.edu
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Degrees

PhD in Biostatistics, University of Minnesota

Bio

Dr. Quick received his PhD from the Division of Biostatistics at the University of Minnesota in 2013, where his research focused on Bayesian methods for spatial and spatiotemporal data analysis.  In addition, Dr. Quick has conducted research in the fields of data confidentiality, spatial epidemiology, and the application of Bayesian methods for environmental health and occupational exposure assessment.

Research Interests

  • Occupational Health
  • Spatial Analysis or GIS
  • Statistical Modeling
  • Urban Health
  • Bayesian Inference
  • Data Confidentiality

Publications

Quick, H. and Waller, L.A. (2018).  "Using spatiotemporal models to generate synthetic data for public use." Spatial Spatio-temporal Epidemiol., 27, 37-45.

Quick, H., Holan, S.H., and Wikle, C.K. (2018). “Generating partially synthetic geocoded public use data with decreased disclosure risk using differential smoothing.” J. Roy. Statist. Soc., Ser. A (Statistics in Society), 181, 649-661.

Quick, H., Waller, L.A., and Casper, M. (2018). “A multivariate space-time model for analyzing county-level heart disease death rates by race and sex.” J. Roy. Statist. Soc., Ser. C (Applied Statistics), 67, 291-304.

Quick, H., Waller, L.A., and Casper, M. (2017). “Multivariate spatiotemporal modeling of age-specific stroke mortality.” Ann. Appl. Stat11, 2170-2182.

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.

Quick, H., Carlin, B.P., and Banerjee, S. (2015).  “Heteroscedastic conditional auto-regression models for areally referenced temporal processes for analysing California asthma hospitalization data.” J. Roy. Statist. Soc., Ser. C (Applied Statistics), 64, 799-813.

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.

Quick, H., Banerjee, S., and Carlin, B.P. (2015).  “Bayesian modeling and analysis for gradients in spatiotemporal processes.”  Biometrics, 71, 575-584.

Visit Dr. Quick's personal website