David V. Hinkley
David Victor Hinkley is a statistician known for his research in statistical models and inference and for his graduate-level books.
Research and graduate textbooks
He earned a PhD from the Imperial College London under the supervision of David R. Cox. In 1974 Hinkley and Cox published a textbook on statistical inference, of unusual breadth and conceptual interest.
Hinkley has also collaborated with Bradley Efron, in particular on writing a paper on maximizing the conditional likelihood function and on using the observed Fisher information.[1]
Bootstrapping
Hinkley is one of the world's foremost experts on bootstrapping, a method of computational statistics, which is largely due to Bradley Efron. With Anthony C. Davison, Hinkley wrote a text on bootstrapping; their text, "Davison-Hinkley", is widely used and referenced.[2]
Positions and awards
Hinkley has been professor at the University of Minnesota, Departments of Applied Statistics and Theoretical Statistics, at the University of Austin, Texas, and at the University of Oxford, U.K. He currently is a professor of statistics at the University of California at Santa Barbara Department of Statistics.[3]
In 1984 Hinkley received the COPSS Presidents' Award.[4]
Notes
- ↑ Efron, B.; Hinkley, D.V. (1978). "Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher Information". Biometrika. 65 (3): 457–487. doi:10.1093/biomet/65.3.457. JSTOR 2335893. MR 0521817.
- ↑ Davison, A. C.; Hinkley, D. (2006). Bootstrap Methods and their Application (8th printing). Cambridge: Cambridge Series in Statistical and Probabilistic Mathematics.]
- ↑ Faculty webpage
- ↑ COPPS Awards – Recipients.
References
- Davison, A. C.; Hinkley, D. (2006). Bootstrap Methods and their Application (8th printing). Cambridge: Cambridge Series in Statistical and Probabilistic Mathematics.
- Efron, B.; Hinkley, D.V. (1978). "Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher Information". Biometrika. 65 (3): 457–487. doi:10.1093/biomet/65.3.457. JSTOR 2335893. MR 0521817.