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Bayesian Predictive Inference for Some Linear Models under Student-t Errors (Paperback)
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Bayesian Predictive Inference for Some Linear Models under Student-t Errors (Paperback)
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In real life often we need to make inferences about the behaviour
of the unobserved responses for a model based on the observed
responses from the model. Regression models with normal errors are
commonly considered in prediction problems. However, when the
underlying distributions have heavier tails, the normal errors
assumption fails to allow sufficient probability in the tail areas
to make allowance for any extreme value or outliers. As well, it
cannot deal with the uncorrelated but not independent observations
which are common in time series and econometric studies. In such
situations, the Student-t errors assumption is appropriate.
Traditionally, a number of statistical methods such as the
classical, structural distribution and structural relations
approaches can lead to prediction distributions, the Bayesian
approach is more sound in statistical theory. This book, therefore,
deals with the derivation problems of prediction distributions for
some widely used linear models having Student-t errors under the
Bayesian approach. Results reveal that our models are robust and
the Bayesian approach is competitive with traditional methods. In
perturbation analysis, process control, optimization,
classification, discordancy testing, interim analysis, speech
recognition, online environmental learning and sampling curtailment
studies predictive inferences are successfully used.
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