The main objective of present paper is to consider robust Bayesian analysis of the Weibull failure model under an <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\varepsilon $$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi mathvariant="italic">ε</mi> </math> </EquationSource> </InlineEquation>-contamination class of priors for the parameters. The Bayes estimators for the mean life, reliability function and failure rate are obtained under the...</equationsource></equationsource></inlineequation>

This paper deals with the problem of Stein-rule prediction in a general linear model. Our study extends the work of Gotway and Cressie (1993) by assuming that the covariance matrix of the model's disturbances is unknown. Also, predictions are based on a composite target function that...

In this paper, we consider a family of feasible generalised double k-class estimators in a linear regression model with non-spherical disturbances. We derive the large sample asymptotic distribution of the proposed family of estimators and compare its performance with the feasible generalized...

In this article, a family of feasible generalized double k-class estimator in a linear regression model with non-spherical disturbances is considered. The performance of this estimator is judged with feasible generalized least-squares and feasible generalized Stein-rule estimators under balanced...

The present paper considers the testing of unit root hypothesis for an autoregressive model with polynomial trend under Bayesian framework. Under the unit root hypothesis the trend component does not vanish completely and its degree reduces by one. The posterior odds ratio for the unit root...

This paper considers a general family of Stein rule estimators for the coefficient vector of a linear regression model with nonspherical disturbances, and derives estimators for the Mean Squared Error (MSE) matrix, and risk under quadratic loss for this family of estimators. The confidence...

In this present paper, considering a linear regression model with nonspherical disturbances, improved confidence sets for the regression coefficients vector are developed using the Stein rule estimators. We derive the large-sample approximations for the coverage probabilities and the expected...

The present article considers Bayesian unit root test for autoregressive model involving structural break in variance. The posterior odds ratio for testing of unit root hypothesis against the alternative of break in variance has been derived under appropriate prior assumptions for the...

The paper develops a general Bayesian framework for robust linear static panel data models using ε-contamination. A two-step approach is employed to derive the conditional type-II maximum likelihood (ML-II) posterior distribution of the coefficients and individual effects. The ML-II posterior...