Some additional works not listed here can be found on Google Scholar and at HPCF Technical Reports. My ORCID is 0000-0002-4440-2330.

Manuscripts

Bibtex entries for the following are here.

  1. Raim, A. M., Livsey, J. A., & Irimata, K. M. (2024+). Rejection sampling with vertical weighted strips. [preprint].

  2. Raim, A. M., Livsey, J. A., & Irimata, K. M. (2024+). Vertical Weighted Strips in R. (In Preparation).

Selected Journal Articles

Bibtex entries for the following are here.

  1. Andrew M. Raim (2023). Direct sampling with a step function. Statistics and Computing, 33(1). [preprint] [doi].

  2. Andrew M. Raim, Thomas Mathew, Kimberly F. Sellers, Renee Ellis, and Mikelyn Meyers (2023). Design and sample size determination for experiments on nonresponse followup using a sequential regression model. Journal of Official Statistics, 39(2), 173-202. [preprint] [doi].

  3. Andrew M. Raim, Elizabeth Nichols, and Thomas Mathew (2023). A statistical comparison of call volume uniformity due to mailing strategy. Journal of Official Statistics, 39(1), 103-121. [preprint] [doi].

  4. Ryan Janicki, Andrew M. Raim, Scott H. Holan, and Jerry J. Maples (2022). Bayesian nonparametric multivariate spatial mixture mixed effects models with application to American Community Survey special tabulations. The Annals of Applied Statistics, 16(1), 144-168. [preprint] [doi].

  5. Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, and Christopher K. Wikle (2021). Spatio-temporal change of support modeling with R. Computational Statistics, 36(1), 749-780. [preprint] [doi].

  6. Sean Martin, Andrew Raim, Wen Huang, and Kofi Adragni (2020). ManifoldOptim: An R interface to the ROPTLIB library for Riemannian manifold optimization. Journal of Statistical Software, 93(1), 1-32. [doi].

  7. Darcy Steeg Morris, Andrew M. Raim, and Kimberly F. Sellers (2020). A Conway-Maxwell-multinomial distribution for flexible modeling of clustered categorical data. Journal of Multivariate Analysis, 179, 104651. [preprint] [doi].

  8. Sai K. Popuri, Andrew M. Raim, Nagaraj K. Neerchal, and Matthias K. Gobbert (2018). Parallelizing computation of expected values in recombinant binomial trees. Journal of Statistical Computation and Simulation, 88(4), 657-674. [preprint] [doi].

  9. Andrew M. Raim, Nagaraj K. Neerchal, and Jorge G. Morel (2018). An extension of generalized linear models to finite mixture outcome distributions. Journal of Computational and Graphical Statistics, 27(3), 587-601. [preprint] [doi].

  10. Andrew M. Raim, Nagaraj K. Neerchal, and Jorge G. Morel (2017). An approximation to the information matrix of exponential family finite mixtures. Annals of the Institute of Statistical Mathematics, 69(2), 333-364. [preprint] [doi].

  11. Derek S. Young, Andrew M. Raim, and Nancy R. Johnson (2017). Zero-inflated modelling for characterizing coverage errors of extracts from the US Census Bureau’s Master Address File. Journal of the Royal Statistical Society: Series A, 180(1), 73-97. [doi].

  12. Kofi P. Adragni, Elias Al-Najjar, Sean Martin, Sai K. Popuri, and Andrew M. Raim (2016). Group-wise sufficient dimension reduction with principal fitted components. Computational Statistics, 31(3), 923-941. [doi].

  13. Kimberly F. Sellers and Andrew Raim (2016). A flexible zero-inflated model to address data dispersion. Computational Statistics and Data Analysis, 99, 68-80. [preprint] [doi].

  14. Kofi Placid Adragni and Andrew M. Raim (2014). ldr: An R software package for likelihood-based sufficient dimension reduction. Journal of Statistical Software, 61(3). [doi].

  15. Andrew M. Raim, Minglei Liu, Nagaraj K. Neerchal, and Jorge G. Morel (2014). On the method of approximate Fisher scoring for finite mixtures of multinomials. Statistical Methodology, 18, 115-130. [preprint] [doi].

  16. Andrew M. Raim, Matthias K. Gobbert, Nagaraj K. Neerchal, and Jorge G. Morel (2013). Maximum-likelihood estimation of the random-clumped multinomial model as a prototype problem for large-scale statistical computing. Journal of Statistical Computation and Simulation, 83(12), 2178-2194. [preprint] [doi].

Selected Reports, Proceedings, and Others

Bibtex entries for the following are here.

  1. Andrew M. Raim (2024). fntl: Numerical Tools for Rcpp and Lambda Functions. Research Report Series: Computing #2024-01. Center for Statistical Research and Methodology, U.S. Census Bureau. [url].

  2. Andrew M. Raim, Renee Ellis, & Mikelyn Meyers. (2024). A multinomial analysis of bilingual training and nonresponse followup contact rates in the 2020 Decennial Census. Study Series: Statistics #2024-01. Center for Statistical Research and Methodology, U.S. Census Bureau. [url].

  3. Darcy Steeg Morris and Andrew M. Raim (2023). Comparing trial and variable association in contingency table data using multinomial models for clustered data. In Elisabeth Bergherr, Andreas Groll, and Andreas Mayr, editors, 37th International Workshop on Statistical Modelling, pages 536-542. [url].

  4. Andrew M. Raim and Elizabeth Nichols (2023). A comparison of map usability via bivariate ordinal analysis. Study Series: Statistics #2023-01. Center for Statistical Research and Methodology, U.S. Census Bureau. [url].

  5. Andrew M. Raim and Kimberly F. Sellers (2022). COMPoissonReg: Usage, the normalizing constant, and other computational details. Research Report Series: Computing #2022-01. Center for Statistical Research and Methodology, U.S. Census Bureau. [url].

  6. Kyle M. Irimata, Andrew M. Raim, Ryan Janicki, James A. Livsey, and Scott H. Holan (2022). Evaluation of Bayesian hierarchical models of differentially private data based on an approximate data model. Research Report Series: Statistics #2022-05. Center for Statistical Research and Methodology, U.S. Census Bureau. [url].

  7. Andrew M. Raim, James A. Livsey, and Kyle M. Irimata (2022). Browsing the 2010 Census SF2 summary file with R. Study Series: Computing #2022-01. Center for Statistical Research and Methodology, U.S. Census Bureau. [url].

  8. Andrew M. Raim (2021). Direct sampling in Bayesian regression models with additive disclosure avoidance noise. Research Report Series: Statistics #2021-01. Center for Statistical Research and Methodology, U.S. Census Bureau. [url].

  9. Elizabeth Nichols, Erica Olmsted-Hawala, Andrew Raim, and Lin Wang (2020). Attitudinal and behavioral differences between older and younger adults using mobile devices. In Human aspects of IT for the aged population. Technologies, design and user experience. Springer Nature Switzerland AG. [doi].

  10. Elizabeth Nichols, Sarah Konya, Rachel Horwitz, and Andrew Raim (2019). 2020 census research and testing report: The effect of the mail delivery date on survey login rates and helpline call rates. U.S. Census Bureau, Research and Methodology Directorate, Center for Behavioral Science Methods Research Report Series (Survey Methodology) #2019-01. U.S. Census Bureau. [url].

  11. Darcy Steeg Morris, Andrew M. Raim, and Kimberly F. Sellers (2018). Introducing a Conway-Maxwell-multinomial distribution for flexible modeling of categorical data. In JSM Proceedings, Biometrics Section, pages 716-733, Alexandria, VA. American Statistical Association. [preprint].

  12. Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, and Christopher K. Wikle (2017). A model selection study for spatio-temporal change of support. In JSM Proceedings, Government Statistics Section, pages 1524-1540, Alexandria, VA. American Statistical Association. [preprint].

  13. Krista Heim and Andrew M. Raim (2016). Predicting coverage error on the Master Address File using spatial modeling methods at the block level. In JSM Proceedings, Survey Research Methods Section, pages 1541-1555, Alexandria, VA. American Statistical Association. [preprint].

  14. Andrew M. Raim (2016). Informing maintenance to the U.S. Census Bureau’s Master Address File with statistical decision theory. In JSM Proceedings, Government Statistics Section, pages 648-659, Alexandria, VA. American Statistical Association. [preprint].

  15. Andrew M. Raim and Marissa N. Gargano (2015). Selection of predictors to model coverage errors in the Master Address File. Research Report Series: Statistics #2015-04. Center for Statistical Research and Methodology, U.S. Census Bureau. [url].

  16. Andrew M. Raim, Marissa N. Gargano, Nagaraj K. Neerchal, and Jorge G. Morel (2015). Bayesian analysis of overdispersed binomial data using mixture link regression. In JSM Proceedings, Statistical Computing Section, pages 2794-2808, Alexandria, VA, 2015. American Statistical Association. [preprint].

  17. Andrew M. Raim, Nagaraj K. Neerchal, and Jorge G. Morel (2015). Modeling overdispersion in R. Technical Report HPCF-2015-1. UMBC High Performance Computing Facility, University of Maryland, Baltimore County. [preprint] [github].

  18. Andrew M. Raim (2014). Computational methods in finite mixtures using approximate information and regression linked to the mixture mean. Ph.D. Thesis, Department of Mathematics and Statistics, University of Maryland, Baltimore County. [pdf] [url].

  19. Andrew M. Raim, Nagaraj K. Neerchal, and Jorge G. Morel (2014). Large cluster approximation to the finite mixture information matrix with an application to meta-analysis. In JSM Proceedings, Statistical Computing Section, pages 4025-4037, Alexandria, VA. American Statistical Association. [preprint].

  20. Andrew M. Raim (2013). Introduction to distributed computing with pbdR at the UMBC High Performance Computing Facility.Technical Report HPCF-2013-2. UMBC High Performance Computing Facility, University of Maryland, Baltimore County. [files] [url].

  21. Andrew M. Raim and Nagaraj K. Neerchal (2013). Modeling overdispersion in binomial data with regression linked to a finite mixture probability of success. In JSM Proceedings, Statistical Computing Section, pages 2760-2774, Alexandria, VA. American Statistical Association. [preprint].

  22. Andrew M. Raim, Brandon E. Fleming, and Nagaraj K. Neerchal (2012). An analysis of categorical injury data using mixtures of multinomials. In JSM Proceedings, Statistical Computing Section, pages 2444-2458, Alexandria, VA. American Statistical Association. [preprint].

  23. Andrew M. Raim and Matthias K. Gobbert (2010). Parallel performance studies for an elliptic test problem on the cluster tara. Technical Report HPCF-2010-2. UMBC High Performance Computing Facility, University of Maryland, Baltimore County. [preprint].