Bayesian literature, with many variations and some preference for two versions labelled pppost and pcpred. The bootstrap method … develop: an ancillary based p-value designated panc; a special version of the Bayesian pcpred; and a bootstrap based p …. For implementation the Bayesian and likelihood procedures would perhaps require the same numerical computations, while the …

intervals: Delta, Fieller, estimated first derivative, bootstrapping, Bayesian and likelihood ratio. We propose interpretations … assessed by Monte Carlo; the Delta and studentized bootstrap can perform quite poorly. Of all the methods, the first derivative …

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional …

A message coming out of the recent Bayesian literature on cointegration is that it is important to elicit a prior on …

strategy is to improve the Minnesota prior, which is frequently used for Bayesian VAR models. The improvement is achieved in …

Bayesian methodology it will be possible to create a comprehensive predictive model of the health burdens being faced by … contributions of this thesis is coherent incorporation of prior information into the proposed expert model. The Bayesian models were …

attention. This paper, for the first time applies a Bayesian methodology to generate disaggregate spatial forecasts of … to 25 fires per year. The application of the Bayesian forecast methodology has the potential to inform policy decisions …

via Monte Carlo integration. In the application to time series models, we propose a Bayesian approach to the robust …

sample theoretical justifications of the semiparametric AFT model. Chapter 3 proposes a semiparametric Bayesian estimation …