The maximum of Brownian motion minus a parabola

We derive a simple integral representation for the distribution of the maximum of Brownian motion minus a parabola, which can be used for computing the density and moments of the distribution, both for one-sided and two-sided Brownian motion.Comment: 7 pages, 4 figures, to appear in the Electronic Journal of Probabilit

Sharing learning experiences through correspondence on the WWW

Asynchronous learning networks are facilities and procedures to allow members of learning communities to be more effective and efficient in their learning. One approach is to see how the `sharing' of knowledge can be augmented through meta-data descriptions attached to portfolios and project work. Another approach is to facilitate the reflection upon individual or collaborative learning experiences (Okamoto, Cristea, Matsui, & Miwata, 2000). The position that I defend in this paper is that both the meta-data approach and the attempts to capture the students' meta-knowledge are rather complicated because of social and emotional reason

The remaining area of the convex hull of a Poisson process

In Cabo and Groeneboom (1994) the remaining area of the left-lower convex hull of a Poisson point process with intensity one in the first quadrant of the plane was analyzed, using the methods of Groeneboom (1988), giving formulas for the expectation and variance of the remaining area for a finite interval of slopes of the boundary of the convex hull. However, the time inversion argument of Groeneboom (1988) was not correctly applied in Cabo and Groeneboom (1994), leading to an incorrect scaling constant for the variance. The purpose of this note is to show how the correct application of the time inversion argument gives the right expression, which is in accordance with results in Nagaev and Khamdamov (1991) and Buchta (2003).Comment: 7 pages, 3 figure

The bivariate current status model

For the univariate current status and, more generally, the interval censoring model, distribution theory has been developed for the maximum likelihood estimator (MLE) and smoothed maximum likelihood estimator (SMLE) of the unknown distribution function, see, e.g., [12], [7], [4], [5], [6], [10], [11] and [8]. For the bivariate current status and interval censoring models distribution theory of this type is still absent and even the rate at which we can expect reasonable estimators to converge is unknown. We define a purely discrete plug-in estimator of the distribution function which locally converges at rate n^{1/3} and derive its (normal) limit distribution. Unlike the MLE or SMLE, this estimator is not a proper distribution function. Since the estimator is purely discrete, it demonstrates that the n^{1/3} convergence rate is in principle possible for the MLE, but whether this actually holds for the MLE is still an open problem. If the cube root n rate holds for the MLE, this would mean that the local 1-dimensional rate of the MLE continues to hold in dimension 2, a (perhaps) somewhat surprising result. The simulation results do not seem to be in contradiction with this assumption, however. We compare the behavior of the plug-in estimator with the behavior of the MLE on a sieve and the SMLE in a simulation study. This indicates that the plug-in estimator and the SMLE have a smaller variance but a larger bias than the sieved MLE. The SMLE is conjectured to have a n^{1/3}-rate of convergence if we use bandwidths of order n^{-1/6}. We derive its (normal) limit distribution, using this assumption. Finally, we demonstrate the behavior of the MLE and SMLE for the bivariate interval censored data of [1], which have been discussed by many authors, see e.g., [18], [3], [2] and [15].Comment: 18 pages, 7 figures, 4 table