Alan E. Gelfand Explained

Alan Enoch Gelfand
Birth Place:Bronx, New York
Workplaces:University of Connecticut
Duke University
Education:City College of New York
Stanford University
Thesis Title:Seriation of Multivariate Observations through Similarities
Thesis Url:https://www.proquest.com/openview/3b6104774e36fef0bb5c0a5db62e82d5/1?pq-origsite=gscholar&cbl=18750&diss=y
Thesis Year:1969
Doctoral Advisor:Herbert Solomon
Doctoral Students:
Known For:Gibbs sampling

Alan Enoch Gelfand (born April 17, 1945) 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.

Education and career

Gelfand was born in Bronx, New York. After graduating from the public school system at the young age of 16, Gelfand attended the City College of New York 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 Solomon.[3]

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.In 2015, his department threw a birthday conference April 19–22 in Durham, North Carolina that included eminent speakers such as Adrian F. M. Smith.[4]

Research

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.[5] To date, the paper has been cited over 7500 times.[6]

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 Bradley P. Carlin, created an inferential paradigm for analyzing spatial data. Gelfand’s contributions to spatial statistics include spatially-varying coefficient models,[7] linear models of coregionalization for multivariate spatial processes,[8] predictive processes for analysis of large spatial data[9] and non-parametric approaches to the analysis of spatial data.[10] Gelfand's research in spatial statistics spans application areas of ecology, disease and the environment.

Awards and recognitions

Bibliography

Books

Selected papers

Notes and References

  1. Web site: Home Page of Alan E. Gelfand. www2.stat.duke.edu. 10 March 2017.
  2. Web site: Alan E. Gelfand. scholars.duke.edu. 10 March 2017. en.
  3. Carlin. Brad. Herring. Amy. 2015. A Conversation with Alan Gelfand. Statistical Science. 30. 3. 413–422. 10.1214/15-sts521. free. 1509.03068.
  4. Web site: G70: A Celebration of Alan Gelfand's 70th Birthday .
  5. Book: McGrayne, Sharon. 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. 2011.
  6. Gelfand . Alan E. . Smith . Adrian F. M. . 1990 . Sampling-Based Approaches to Calculating Marginal Densities . Journal of the American Statistical Association . 85 . 410 . 398–409 . 10.2307/2289776 . 2289776 . 0162-1459.
  7. Gelfand. Alan. Spatial modeling with spatially varying coefficient processes. Journal of the American Statistical Association. 98. 462. 387–396. 10.1198/016214503000170. 2003. 122987154.
  8. Gelfand. Alan. Nonstationary multivariate process modeling through spatially varying coregionalization. Test. 13. 2. 263–312. 10.1007/bf02595775. 2004. 56244076 .
  9. Banerjee. Sudipto. Gaussian predictive process models for large spatial data sets. Journal of the Royal Statistical Society. Series B (Statistical Methodology). 70. 4. 825–848. 10.1111/j.1467-9868.2008.00663.x. 19750209. 2741335. 2008.
  10. Gelfand. Alan. Bayesian nonparametric spatial modeling with Dirichlet process mixing. Journal of the American Statistical Association. 100. 471. 1021–1035. 10.1198/016214504000002078. 2005. 35557355 .
  11. Web site: 2006 . Boston Chapter of the American Statistical Association Newsletter .
  12. Web site: The Parzen Prize for Statistical Innovation . 2023-05-13 . parzenprize.gandi.ws . en.
  13. Web site: ENVR Awards (Distinguished Achievement Award and Early Investigator Award) . 2023-10-22 . community.amstat.org . en.
  14. Web site: ISBA fellows . bayesian.org.
  15. Web site: Samuel S. Wilks Memorial Award. www.amstat.org . en.
  16. Web site: Best Scientists - Mathematics Alan E. Gelfand . research.com.

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