In this paper, we show how to exactly sample from the distribution of the maximum of a random walk with negative drift. We also explore related variance reduction methods.

We propose a method for extending a given asset pricing formula to account for two additional sources of risk: the risk associated with future changes in market–calibrated parameters and the remaining risk associated with idiosyncratic variations in the individual assets described by the formula. The paper makes simple and natural assumptions for how these risks behave. These extra risks should...

Concerning a single major league at bat, the percentage of variance in batting performance attributable to skill differentials among major league baseball players can be calculated statistically. The statistically appropriate calculation is seriously discrepant with intuitions about the influence of skill in batting performance. This paradoxical discrepancy is discussed in terms of habits of th...

The reverberation time is a key feature for describing the acoustic properties of a reverberant room. It can be computed from a measured Room Impulse Response but in many applications it has to be estimated blindly. Existing blind methods give accurate estimates but they often exhibit high variance across different speakers. In this paper, a low variance blind estimator of the reverberation tim...

The complexity of computing conventional matrix multiply wave-front reconstructors scales as O(n3) for most adaptive optical (AO) systems, where n is the number of deformable mirror (DM) actuators. This is impractical for proposed systems with extremely large n. It is known that sparse matrix methods improve this scaling for least-squares reconstructors, but sparse techniques are not immediatel...

The Monte Carlo method exhibits generality and insensitivity to the number of stochastic variables, but is expensive for accurate yield estimation of electronic circuits. In the literature, several variance reduction techniques have been described, e.g., stratified sampling. In this contribution the theoretical aspects of the partitioning scheme of the tolerance region in stratified sampling is...

In this paper the performance of bagging in classification problems is theoretically analysed, using a framework developed in works by Tumer and Ghosh and extended by the authors. A bias-variance decomposition is derived, which relates the expected misclassification probability attained by linearly combining classifiers trained on N bootstrap replicates of a fixed training set to that attained ...