This paper describes the Bayesian inference and prediction of the inverse Weibull distribution for Type-II censored data. First we consider the Bayesian inference of the unknown parameter under a squared error loss function. Although we have discussed mainly the squared error loss function, any other loss function can easily be considered. Gibbs sampling procedure is used to draw Markov Chain M...

• This work considers a generalization of the INAR(1) model to the panel data first order Seemingly Unrelated INteger AutoRegressive Poisson model, SUINAR(1). It presents Bayesian and classical methodologies to estimate the parameters of Poisson SUINAR(1) model and to forecast future observations of the process. In particular, prediction intervals for forecasts — classical approach — and HPD pr...

Conformal prediction is a method of producing prediction sets that can be applied on top of a wide range of prediction algorithms. The method has a guaranteed coverage probability under the standard IID assumption regardless of whether the assumptions (often considerably more restrictive) of the underlying algorithm are satisfied. However, for the method to be really useful it is desirable that...

Neural networks can be viewed as nonlinear models, where the weights are parameters to be estimated. In general two parameter estimation methods are used: nonlinear regression, corresponding to the standard backpropagation algorithm, and Bayesian estimation, in which the model parameters are considered as being random variables drawn from a prior distribution, which is updated based on the obse...

In this paper, the estimation problem of generalized Rayleigh distribution is considered. The parameters are estimated using likelihood based inferential procedure: classical as well as Bayesian. We have computed MLEs and Bayes estimates under gamma priors along with their asymptotic confidence, bootstrap and HPD intervals. The Bayesian estimates of the parameters of generalized Rayleigh distri...

Computing prediction intervals (P.I.s) is an important part of the forecasting process intended s i to indicate the likely uncertainty in point forecasts. The commonest method of calculating P.I. s to use theoretical formulae conditional on a best-fitting model. If a normality assumption is t o used, it needs to be checked. Alternative computational procedures that are not so dependen n a fitte...