Minimax and Bayes estimation when the loss function is unknown
Minimum distance estimator for a hyperbolic stochastic partial differentialequation
We study a minimum distance estimator in -norm for a class ofnonlinear hyperbolic stochastic partial differential equations, driven by atwo-parameter white noise. The consistency and asymptotic normality of thisestimator are established under some regularity conditions on thecoefficients. Our results are applied to the two-parameterOrnstein-Uhlenbeck process.
Moment estimation of customer loss rates from transactional data.
Monte Carlo simulation of Markov chain steady-state distribution.