Bayesian optimization analysis with ML-II ε-contaminated prior

In this paper we derive the predictive density function of a future observation when prior distribution for unknown mean of a normal population is a Type-II maximum likelihood ε-contaminated prior. The derived predictive distribution is applied to the problem of optimization of a regression nature in the decisive prediction framework.     

Robust weighted Cramér-von Mises Estimators of location,with minimax variance in ϵ-contamination neighbourhoods

We construct weighted Cramér-von Mises location estimators which are asymptotically normally distributed throughout an ?e-contamination neighbourhood of a given, strongly unimodal distribution function, and which minimize the maximum asymptotic variance in such neighbourhoods. Applications to the estimation of a normal or logistic mean are given.