Joshua Landon

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Joshua Landon

Assistant Professor; Undergraduate Program Director


Department: Statistics

Contact:

Email: Joshua Landon
Office Phone: (202) 994-7851
801 22nd St NW Washington, DC 20052

Areas of Expertise include: Bayesian Statistics, Stochastic Processes, Markov Chain Monte Carlo Methods, Decision Analysis, Reliability and Risk Analysis


Bayesian Statistics, Stochastic Processes, Markov Chain Monte Carlo Methods, Decision Analysis, Reliability and Risk Analysis

MBAD 6220 – Statistical Analysis for Managers 
STAT 6282 – Foundations of Risk Analysis 
STAT 6210 – Data Analysis 
STAT 4198 – Introduction to Bayesian Statistics 
STAT 4197 – Fundamentals of SAS Programming for Data Management 
STAT 4181 – Applied Time Series Analysis
STAT 2183 – Statistical Computing Packages 
STAT 2118 – Regression Analysis 
STAT 2112 – Business and Economic Statistics II
STAT 1111 – Business and Economic Statistics I 
STAT 1053 – Introduction to Statistics in Social Science
STAT 1051 – Introduction to Statistics in Business and Economics

•    Landon, J. and Gastwirth G. (2022). Graphical Measures Summarizing the Inequality of Income of Two Groups, Statistics and Public Policy, Vol. 9(1), 20-25. https://doi.org/10.1080/2330443X.2021.2016084

•    Ay, A., Landon, J., Ruggeri F. and Soyer, R. (2022). A Latent-Factor Self-Exciting Point Process for Software, European Journal of Operational Research, Recently Submitted, Currently Under Review.

•    Landon, J. (2022). A Bayesian Analysis of Raman Spectrums, Technometrics, Recently Submitted, Currently Under Review.

•    Ay, A., Landon, J, Ozekici, S. and Soyer, R. (2021). Bayesian Analysis of Doubly Stochastic Markov Processes in Reliability, Probability in the Engineering and Informational Sciences, Vol. 35(3), 708-729. https://doi.org/10.1017/S0269964820000157

•    Landon, J., Ozekici, S. and Soyer, R. (2019). Bayesian Analysis of Markov Modulated Queues with Abandonment, Applied Stochastic Models in Business and Industry, Under Revision.

•    Landon, J. (2016). Book Review of "SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition".  The American Statistician, Vol. 70(3). https://doi.org/10.1080/00031305.2016.1203696

•    Landon, J. and Singpurwalla, N. (2014). Solving a System of High Dimensional Equations by MCMC, Perspectives on High Dimensional Data Analysis: Contemporary Mathematics, American Mathematical Society, Vol. 622, 11-20. https://doi.org/10.1090/conm/622/12430

•    Landon, J., Ozekici, S. and Soyer, R. (2013). A Markov Modulated Poisson Model for Software Reliability (with S. Ozekici and R. Soyer), European Journal of Operational Research, Vol. 229(2), 404-410. https://doi.org/10.1016/j.ejor.2013.03.014

•    Landon, J., Lee, F. and Singpurwalla, N. D. (2011). A Problem in Particle Physics and Its Bayesian Solution, Statistical Science, Vol. 26(3), 352-368. https://doi.org/10.1214/11-STS364

•    Landon, J., Ruggeri, F., Soyer, R., and Tarimcilar, M. (2010). Modeling Latent Sources in Call Center Arrival Data, European Journal of Operational Research, Vol. 204(3), 597-603. https://doi.org/10.1016/j.ejor.2009.10.022

•    Landon, J. and Singpurwalla, N. D. (2008). Choosing a Coverage Probability for Prediction Intervals, American Statistician, Vol. 62(2), 120-124. https://doi.org/10.1198/000313008X304062

•    Chandra, J. and Landon, J. (2006). Towards a Reliable and Resilient Mobile Wireless Architecture, Stochastic Analysis and Applications, Vol. 24(4), 827-841. https://doi.org/10.1080/07362990600753569

Ph.D. in Statistics, The George Washington University, 2007.
B.A. in Mathematics, University of Oxford, 2000.