Vector exponential models and second order inference. | [Pakistan Journal of Statistics and Operation Research • 2012]

Author(s):

1. D. A. S. Fraser: Department of Statistics, University of Toronto, Toronto, Ontario, Canada M5S 3G3

2. Uyen Hoang: Department of Statistics, University of Toronto, Toronto, Ontario, Canada M5S 3G3

3. Kexin Ji: Department of Statistics, University of Toronto, Toronto, Ontario, Canada M5S 3G3

4. Xufei Li: Department of Statistics, University of Toronto, Toronto, Ontario, Canada M5S 3G3

5. Li Li: Department of Statistics, University of Toronto, Toronto, Ontario, Canada M5S 3G3

6. Wei Lin: Department of Statistics, University of Toronto, Toronto, Ontario, Canada M5S 3G3

7. Jie Su: Department of Statistics, University of Toronto, Toronto, Ontario, Canada M5S 3G3

Abstract:

For an exponential model with scalar parameter, WelchP:1963 examined the role of Bayesian analysis in statistical inference, more specifically the use of the Jeffreys:1946 prior. They determined that Bayesian intervals and thus in effect Bayesian quantiles had second order confidence accuracy. We use a Taylor series expansion of the log-model to develop a second order version of the vector exponential model; this is developed as a contribution to theory in statistics at a time when algorithms are prominent, and it provides a basis for generalizing the Welch-Peers approach to the vector parameter context.

Page(s): 433-440

DOI: DOI not available

Published: Journal: Pakistan Journal of Statistics and Operation Research, Volume: 8, Issue: 3, Year: 2012

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