Alan E. Gelfand

Alan E. Gelfand is an American statistician, and is currently the James B. Duke Professor of Statistics and Decision Sciences at Duke University.[1][2] Gelfand’s research includes substantial contributions to the fields of Bayesian statistics, spatial statistics and hierarchical modeling.

Early life and education

Alan E. Gelfand was born April 17, 1945 in Bronx, NY. After graduating from the public school system at the young age of 16, Gelfand attended the City College of New York (now the City University of New York; CUNY) as an undergraduate where he excelled in mathematics. Gelfand’s matriculation to graduate school symbolized both a physical and educational transition as he moved cross-country to attend Stanford University and pursue a Ph.D. in Statistics. He finished his dissertation in 1969 on seriation methods (chronological sequencing) under the direction of Herbert Soloman.[3]

Career

Gelfand accepted an offer from the University of Connecticut where he spent 33 years as a professor. In 2002, he moved to Duke University as the James B. Duke Professor of Statistics and Decision Sciences.[3]

Gelfand and Smith (1990)

After attending a short course taught by Adrian Smith at Bowling Green State University, Gelfand decided to take a sabbatical to Nottingham, UK with the intention of working on using numerical methods to solve empirical Bayes problems. After studying Tanner and Wong (1987) and being hinted as to its connection to Geman and Geman (1984) by David Clayton, Gelfand was able to realize the computational value of replacing expensive numerical techniques with Monte Carlo sampling-based methods in Bayesian inference. Published as Gelfand and Smith (1990), Gelfand described how the Gibbs sampler can be used for Bayesian inference in a computationally efficient manner. Since its publication, the general methods described in Gelfand and Smith (1990) has revolutionized data analysis allowing previously intractable problems to now be tractable.[4] To date, the paper has been cited over 7500 times.[5]

Contributions to spatial statistics

In 1994, Gelfand was presented with a dataset that he had previously not encountered: scallop catches on the Atlantic Ocean. Intrigued by the challenges associated with analyzing data with structured spatial correlation, Gelfand, along with colleagues Sudipto Banerjee and Brad Carlin, created an inferential paradigm for analyzing spatial data. Gelfand’s contributions to spatial statistics include spatially-varying coefficient models,[6] linear models of coregionalization for multivariate spatial processes,[7] predictive processes for analysis of large spatial data[8] and non-parametric approaches to the analysis of spatial data.[9] Gelfand's research in spatial statistics spans application areas of ecology, disease and the environment.

Awards and recognitions
  • Elected Fellow of the American Statistical Association, May 1978
  • Elected Member of the International Statistical Institute, 1986
  • Elected Member of the Connecticut Academy of Arts and Sciences, April 1995
  • Elected Fellow of the Institute of Mathematical Statistics, August 1996
  • Mosteller Statistician of the Year Award, February 2001
  • Tenth Most Cited Mathematical Scientist in the World 1991-2001
  • Science Watch President, International Society for Bayesian Analysis, 2006
  • Recipient, Parzen Prize, 2006
  • Distinguished Research Medal, ASA Section on Statistics and the Environment, 2013
  • Elected Fellow, International Society for Bayesian Analysis, November 2015

Selected Publications (in Reverse Chronological Order)
  • Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014). Hierarchical modeling and analysis for spatial data. CRC Press.
  • Gelfand, A.E. (2012). "Hierarchical Modeling for Spatial Data Problems". Spatial Statistics. 1: 30–39. doi:10.1016/j.spasta.2012.02.005. PMC 3760588. PMID 24010050.
  • Berrocal, V.J.; Gelfand, A.E; Holland, D.M. (2010). "A Spatio-temporal Downscaler for Output from Numerical Models". Journal of Agricultural, Biological and Environmental Statistics. 14 (2): 176–197. doi:10.1007/s13253-009-0004-z. PMC 2990198. PMID 21113385.
  • Gelfand, A. E., Diggle, P., Guttorp, P., & Fuentes, M. (Eds.). (2010). Handbook of spatial statistics. CRC press.
  • Banerjee, S.; Gelfand, A. E.; Finley, A. O.; Sang, H. (2008). "Gaussian predictive process models for large spatial data sets". Journal of the Royal Statistical Society. Series B (Statistical Methodology). 70 (4): 825–848. doi:10.1111/j.1467-9868.2008.00663.x. PMC 2741335. PMID 19750209.
  • Gelfand, A. E.; Kottas, A.; MacEachern, S. N. (2005). "Bayesian nonparametric spatial modeling with Dirichlet process mixing". Journal of the American Statistical Association. 100 (471): 1021–1035. doi:10.1198/016214504000002078.
  • Gelfand, A. E.; Schmidt, A. M.; Banerjee, S.; Sirmans, C. F. (2004). "Nonstationary multivariate process modeling through spatially varying coregionalization". Test. 13 (2): 263–312. doi:10.1007/bf02595775.
  • Gelfand, A. E.; Kim, H. J.; Sirmans, C. F.; Banerjee, S. (2003). "Spatial modeling with spatially varying coefficient processes". Journal of the American Statistical Association. 98 (462): 387–396. doi:10.1198/016214503000170.
  • Waller, L. A.; Carlin, B. P.; Xia, H.; Gelfand, A. E. (1997). "Hierarchical spatio-temporal mapping of disease rates". Journal of the American Statistical Association. 92 (438): 607–617. doi:10.1080/01621459.1997.10474012.
  • Gelfand, A. E., & Dey, D. K. (1994). Bayesian model choice: asymptotics and exact calculations. Journal of the Royal Statistical Society. Series B (Methodological), 501-514.
  • Gelfand, A. E.; Hills, S. E.; Racine-Poon, A.; Smith, A. F. (1990). "Illustration of Bayesian inference in normal data models using Gibbs sampling". Journal of the American Statistical Association. 85 (412): 972–985. doi:10.1080/01621459.1990.10474968.
  • Gelfand, A. E.; Smith, A. F. (1990). "Sampling-based approaches to calculating marginal densities". Journal of the American Statistical Association. 85 (410): 398–409. doi:10.1080/01621459.1990.10476213.
gollark: Given their access to ND tutorials/assistance, fancy forumy trading whatsits, notifications about, say, AP SAltkins, and other stuff, probably discord/forum people are slightly "richer" than the average DC user.
gollark: The ones on the forum/discord are most likely to be those WITH rares, though.
gollark: See, if an ND-maker can just wait two months and get a gold (assuming this stays in place, though) they'll demand more golds due to their declining rarity/value.
gollark: Yes, BUT the availability of market eggs will drive down demand for them.
gollark: The ~~Flash~~ Ratio Crash?

References
  1. "Home Page of Alan E. Gelfand". www2.stat.duke.edu. Retrieved 10 March 2017.
  2. "Alan E. Gelfand". scholars.duke.edu. Retrieved 10 March 2017.
  3. Carlin, Brad; Herring, Amy (2015). "A Conversation with Alan Gelfand". Statistical Science. 30 (3): 413–422. doi:10.1214/15-sts521.
  4. McGrayne, Sharon (2011). The theory that would not die: how Bayes' rule cracked the enigma code, hunted down Russian submarines & emerged triumphant from two centuries of controversy. Yale University Press.
  5. Gelfand, Alan; Smith, Adrian (May 2018). "Sampling-Based Approaches to Calculating Marginal Densities". Journal of the American Statistical Association. 85 (410): 398–409. doi:10.1080/01621459.1990.10476213.
  6. Gelfand, Alan (2003). "Spatial modeling with spatially varying coefficient processes". Journal of the American Statistical Association. 98 (462): 387–396. doi:10.1198/016214503000170.
  7. Gelfand, Alan (2004). "Nonstationary multivariate process modeling through spatially varying coregionalization". Test. 13 (2): 263–312. doi:10.1007/bf02595775.
  8. Banerjee, Sudipto (2008). "Gaussian predictive process models for large spatial data sets". Journal of the Royal Statistical Society. Series B (Statistical Methodology). 70 (4): 825–848. doi:10.1111/j.1467-9868.2008.00663.x. PMC 2741335. PMID 19750209.
  9. Gelfand, Alan (2005). "Bayesian nonparametric spatial modeling with Dirichlet process mixing". Journal of the American Statistical Association. 100 (471): 1021–1035. doi:10.1198/016214504000002078.
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