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On a subjective modelling of VaR fa Bayesian approach /Siu, Wai-shing. January 2001 (has links)
Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 74-80).
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Bayesian analysis for avian nest survival modelsTra, Yolande Vololonirina, January 2000 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 85-88). Also available on the Internet.
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Bayesian inference for models with monotone densities and hazard rates /Ho, Man Wai. January 2002 (has links)
Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 110-114). Also available in electronic version. Access restricted to campus users.
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Essays on model selection using Bayesian inferenceChen, Guo, January 2009 (has links)
Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Economics." Includes bibliographical references (p. 113-120).
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Online auction price prediction a Bayesian updating framework based on the feedback history /Yang, Boye. January 2009 (has links)
Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 59-62). Also available in print.
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Development of a method for model calibration with non-normal dataWang, Dongyuan. January 2002 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 2002. / Vita. Includes bibliographical references. Available also from UMI Company.
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Analysis of outliers using graphical and quasi-Bayesian methods /Fung, Wing-kam, Tony. January 1987 (has links)
Thesis (Ph. D.)--University of Hong Kong, 1987.
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The effect of alternate information structures on probability revisions /Dickhaut, John Wilson, January 1970 (has links)
Thesis (Ph. D.)--Ohio State University, 1970. / Includes bibliographical references (leaves 185-187). Available online via OhioLINK's ETD Center.
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Bayesian analysis in Markov regime-switching modelsKoh, You Beng., 辜有明. January 2012 (has links)
van Norden and Schaller (1996) develop a standard regime-switching model to study stock market crashes. In their seminal paper, they use the maximum likelihood estimation to estimate the model parameters and show that a two-regime speculative bubble model has significant explanatory power for stock market returns in some observed periods. However, it is well known that the maximum likelihood estimation can lead to bias if the model contains multiple local maximum points or the estimation starts with poor initial values. Therefore, a better approach to estimate the parameters in the regime-switching models is to be found. One possible way is the Bayesian Gibbs-sampling approach, where its advantages are well discussed in Albert and Chib (1993). In this thesis, the Bayesian Gibbs-sampling estimation is examined by using two U.S. stock datasets: CRSP monthly value-weighted index from Jan 1926 to Dec 2010 and S&P 500 index from Jan 1871 to Dec 2010. It is found that the Gibbs-sampling estimation explains the U.S. data better than the maximum likelihood estimation. Moreover, the existing standard regime-switching speculative behaviour model is extended by considering the time-varying transition probabilities which are governed by the first-order Markov chain. It is shown that the time-varying first-order transition probabilities of Markov regime-switching speculative rational bubbles can lead stock market returns to have a second-order Markov regime. In addition, a Bayesian Gibbs-sampling algorithm is developed to estimate the parameters in the second-order two-state Markov regime-switching model. / published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy
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Bayesian variable selection for GLMWang, Xinlei 28 August 2008 (has links)
Not available / text
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