Vector exponential models and second order inference.

D. A. S. Fraser; Uyen Hoang; Kexin Ji; Xufei Li; Li Li; Wei Lin; Jie Su

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Vector exponential models and second order inference.

Author(s)

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

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

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

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

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

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

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.

Publication Details

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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