Michael David Escobar is an American biostatistician who is known for Bayesian nonparametrics, mixture models.

Education and career

Escobar earned a degree in mathematics at Tufts University in 1981 followed by a doctorate in statistics at Yale University in 1988 under the supervision of John Hartigan. Between 1990 and 1994, he was an assistant professor at Carnegie Mellon University.[1] Escobar subsequently joined the University of Toronto faculty.[1][2] In 2015, he was elected a fellow of the American Statistical Association.[3]

Bibliography

  • Escobar, Michael D. (1994). "Estimating Normal Means with a Dirichlet Process Prior". Journal of the American Statistical Association. 89 (425): 268–277. doi:10.1080/01621459.1994.10476468. ISSN 0162-1459.
  • Escobar, Michael D.; West, Mike (1995). "Bayesian Density Estimation and Inference Using Mixtures". Journal of the American Statistical Association. 90 (430): 577–588. doi:10.1080/01621459.1995.10476550. ISSN 0162-1459.
  • Escobar, Michael D.; West, Mike (1998), Dey, Dipak; Müller, Peter; Sinha, Debajyoti (eds.), "Computing Nonparametric Hierarchical Models", Practical Nonparametric and Semiparametric Bayesian Statistics, New York, NY: Springer New York, vol. 133, pp. 1–22, doi:10.1007/978-1-4612-1732-9_1, ISBN 978-0-387-98517-6, retrieved 2023-04-11
  • Austin, Peter C; Escobar, Michael; Kopec, Jacek A (2000). "The use of the Tobit model for analyzing measures of health status". Quality of Life Research. 9 (8): 901–910. doi:10.1023/A:1008938326604.

References

  1. 1 2 "Curriculum Vitae Michael D. Escobar" (PDF). University of Toronto. Retrieved 16 October 2022.
  2. ↑ "Faculty Member Michael Escobar Ph.D." University of Toronto. Retrieved 16 October 2022.
  3. ↑ "ASA Fellows". American Statistical Association. Retrieved 16 October 2022.
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