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Noninformative priors for linear combinations of the normal means
Statistical Papers, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kim, Dal Ho +2 more
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Murphy’s Law and Noninformative Priors
1992In this conversation, a stockbroker offers a client some investment opportunity. But the client has very strong priors against the deal paying off. Hence, even though he has not heard anything specific about the investment, the client knows that he will probably discount the investment opportunity and find it unsatisfactory.
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On Bayesian Inference for Proportional Hazards Models Using Noninformative Priors
Lifetime Data Analysis, 2000In this article, we investigate the properties of the posterior distribution under the uniform improper prior for two commonly used proportional hazards models; the Weibull regression model and the extreme value regression model. We allow the observations to be right censored.
Kim, Seong W., Ibrahim, Joseph G.
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Noninformative Priors for the Bivariate Fieller-Creasy Problem
Statistics & Risk Modeling, 2001Summary: Consider two normal distributions \({\mathcal N}(\mu, \Sigma)\) and \({\mathcal N}(\theta\mu,\Sigma)\). The parameter \(\theta\) is referred to as the magnitudinal effect by \textit{I. Guttman}, \textit{U. Menzefricke} and \textit{D. Tyler} [Ann. Stat. 14, 1555-1571 (1986; Zbl 0617.62033)].
Rousseau, Judith, Ghosh, M., Kim, D.
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Noninformative Priors and Bayesian Testing for the AR(1) Model
Econometric Theory, 1994Various approaches to the development of a noninformative prior for the AR(1) model are considered and compared. Particular attention is given to thereference priorapproach, which seems to work well for the stationary case but encounters difficulties in the explosive case.
James O. Berger, Ruo-Yong Yang
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Noninformative priors for prediction based on group models
AIP Conference Proceedings, 2004Several topics concerning noninformative priors and prediction for group models are considered. The Kullback‐Leibler divergence from the true distribution to a predictive distribution is adopted as a loss function.When a model has a group structure, the right invariant measure is often recommended as a noninformative prior. We consider group models and
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Bayesian Population Estimation for Small Sample Capture-Recapture Data Using Noninformative Priors
Journal of Statistical Planning and Inference, 2007Dongchu Sun
exaly