Minimax and Bayes estimation when the loss function is unknown
Minimax sequential estimation for the multinomial and gamma processes
Minimax sequential estimation of parameters of random fields
Minimum variance importance sampling via Population Monte Carlo
Variance reduction has always been a central issue in Monte Carlo experiments. Population Monte Carlo can be used to this effect, in that a mixture of importance functions, called a D-kernel, can be iteratively optimized to achieve the minimum asymptotic variance for a function of interest among all possible mixtures. The implementation of this iterative scheme is illustrated for the computation of the price of a European option in the Cox-Ingersoll-Ross model. A Central Limit theorem as well...
Multi-Stage Point and Interval Estimation of the Largest Mean of K Normal Populations and the Associated Second-Order Properties.