Michael D. Escobar

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, vol. 133, New York, NY: Springer New York, 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. PMID 11284209.