Project Fact File

Title: Predictive Inference
Category: statistics
Area: Statistical Inference
No. of units: 1 or 2 or 4
Supervisor: Shahjahan Khan (Staff Profile)
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 a 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. Prediction distribution of future parameters are also obtainable. Application of predictive inference includes problems in areas such as tolerance regions, model selection, process control, optimisation, perturbation and many others.

Student: 1