AbstractIn this paper we derive stochastic representations for the finite dimensional distributions of a multidimensional diffusion on a fixed time interval, conditioned on the terminal state. The conditioning can be with respect to a fixed point or more generally with respect to some subset. The representations rely on a reverse process connected with the given (forward) diffusion as introduce...

Stochastic methods of space-time interpolation and conditional simulation have been used in statistical downscaling approaches to increase the resolution of measured fields. One of the popular interpolation methods in geostatistics is kriging, also known as optimal interpolation in data assimilation. Kriging is a stochastic, linear interpolator which incorporates time/space variability by means...

We present in this paper a single-hop version of the conditional-knowledge approach to optimistic simulations 10]. The algorithm has potentially lower memory requirements than the conventional time-warp algorithm and uses a single negative message between processes for cancellation of multiple erroneously sent messages. In time-warp, each processed message is stored both on the output queue of ...

We present in this paper a single-hop version of the conditionalknowledge approach to optimistic simulations [10]. The algorithm has potentially lower memory requirements than the conventional timewarp algorithm and uses a single negative message between processes for cancellation of multiple erroneously sent messages. In time-warp, each processed message is stored both on the output queue of a...

For the regression model Yi = m(xi) + σ(xi)2i, i = 1, . . . , m, we propose a new nonparametric diagnostic test for checking the constancy of the conditional variance function σ(x). The proposed test does not assume a known parametric form for the conditional mean function m(x). The test statistic is inspired by recent asymptotic theory in analysis of variance when the number of factor levels i...

Barndorff-Nielsen and Cox (1994, p.319) modify an estimative prediction limit to obtain an improved prediction limit with better coverage properties. Kabaila and Syuhada (2008) present a simulation-based approximation to this improved prediction limit, which avoids the extensive algebraic manipulations required for this modification. We present a modification of an estimative prediction interva...