We investigate the problem of simultaneous estimation of multivariate normal mean vector using Zellner (1994)’s balance loss function when common variance σ2 is unknown. We first find a class of minimax estimators for this problem which extends a class given by Chung et al. (1999). This...

The problem of estimating the mean vector μ of a multivariate normal distribution with the covariance matrix σ2Ip is considered under the loss function, (δ−μ)′D(δ−μ)σ2, where σ2 is unknown and D is a known positive definite diagonal matrix. A large class of Bayes minimax estimators...

This paper is concerned with the Bayesian estimation of non-linear stochastic differential equations when only discrete observations are available. The estimation is carried out using a tuned MCMC method, in particular a blocked Metropolis-Hastings algorithm, by introducing auxiliary points and...

This paper is concerned with the estimation of stochastic differential equations when only discrete observations are available. It primarily focuses on deriving a closed form solution for the one-step ahead conditional transition density using the Milstein scheme. This higher order Taylor...

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating...