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Research Thesis Topic

Predictive Inference


Topic ID
171

Thesis Topic/Title
Predictive Inference

Description

Prediction distribution is the basis for many predictive inferences. Unlike the common practice of estimating parameters of a model or performing tests of hypotheses regarding the parameters involved, often the aim of a researcher/practitioner is to predict the value of a (or a set of) future response(s) from a given model. The technique of prediction is used in many real world situations as it has common sense appeal and simple interpretation. The prediction distribution is the probability distribution of one or more future (unobserved) responses, conditional on a set of observed responses from the same model. The method is useful in both univariate and multivariate problems. Predictive inference is possible for models with independent as well as dependent and correlated responses. Bayesian and other approaches are adopted for the purpose of predictive inference. Available methods can handle the conventional normal model and non-normal robust models. Application of predictive inference includes problems in areas such as tolerance regions, model selection, process control, optimisation, perturbation and many others. The customary use of the normal model comes under serious question when the population distribution is symmetric but have heavier tails that the normal distribution. Also, the normal model fails to incorporate dependent but uncorrelated responses. In such cases the multivariate Student-t distribution provides an appropriate model for the population. For such models we can obtain the maximum likelihood estimators of the mean and scale parameters of multivariate Student-t distribution. The model has been used to find appropriate test statistic to test the mean vector. The distributions of the sum of squares and product matrix for the multivariate Student-t model as well as the predictive distribution of future model have been proposed. Similar results for the matrix T and elliptically contoured model are also obtained. Both classical and Bayesian approaches can be applied. Projects in this area will extend this previous work.


Principal Supervisor

Associate Supervisors

Research Affiliations
  • School of Agricultural, Computational and Environmental Sciences

Field of Research
  • Other Medical and Health Sciences
  • Statistics


Application Open Date
04/06/2016

Application Close Date
04/10/2019

USQ Scholarship Applications

Pre-approved for Ethics
Not Applicable

Admission Requirements

Please review the admission requirements for the academic program associated with this Thesis Topic




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