Bootstrap tilting con dence intervals could be the method of choice in many applications for reasons of both speed and accuracy With the right implementa tion tilting intervals are times as fast as bootstrap BC a limits in terms of the number of bootstrap sam ples needed for comparable simulation accuracy Thus bootstrap samples might su ce instead of Tilting limits have other desirable properti...

In transportation systems today, there is a need to predict where a vehicle will be at a given time in order to ensure safety, expediency and eeciency of traf-c movement. There is generally a plan of travel, but outside forces (e.g., wind forecasting error, navigation system error) cause the actual path that is followed to be somewhat diierent from the planned path. The path of a vehicle is rep...

Although Decision Trees are widely used for classi cation tasks, they are typically not used for agent control. This paper presents a novel technique for agent control in a complex multiagent domain based on the con dence factors provided by the C4.5 Decision Tree algorithm. Using Robotic Soccer as an example of such a domain, this paper incorporates a previously-trained Decision Tree into a fu...

This paper proposes a new way to construct con…dence sets for a parameter of interest in models comprised of …nitely many moment inequalities. Building on results from the literature on multivariate one-sided tests, I show how to test the hypothesis that any particular parameter value is logically consistent with the maintained moment inequalities. The associated test statistic has an asymptoti...

In this paper we present a framework for testing various algorithms that deal with transpositions in Monte-Carlo Tree Search (MCTS). When using transpositions in MCTS, a Direct Acyclic Graph (DAG) is progressively developed instead of a tree. There are multiple ways to handle the exploration exploitation dilemma when dealing with transpositions. We propose parameterized ways to compute the mean...

Seasonal adjustment methods transform observed time series data into estimated data, where these estimated data are constructed such that they show no or almost no seasonal variation. An advantage of model-based methods is that these can provide con dence intervals around the seasonally adjusted data. One particularly useful time series model for seasonal adjustment is the basic structural time...

The diversity of methods, contents and tests used in the study of eyewitness memory may have contributed to discrepancies in results in this ®eld. In this experiment, using incidental or intentional learning, we examine the recall and recognition of actions and details concerning the central and peripheral information of a kidnapping. A similar pattern emerges in free recall, hits and recogniti...

We propose identi…cation robust inference methods for multivariate reduced rank (MRR) regressions. Such models involve nonlinear restrictions on the coe¢ cients of a multivariate linear regression (MLR), whose identi…cation may raise serious non-regularities leading to the failure of standard asymptotics. To circumvent such problems, we propose con…dence set estimates for parameters of interest...

We consider the problem of resampling or bootstrapping a point process to get con dence intervals for the reduced second moment function We propose a resampling scheme for spatial data which we call the marked point method This is a variant of the block of blocks bootstrap rst introduced by K unsch A simulation study with a Poisson a clustered and a regular point process on the unit square in R...

This paper elaborates a simple and general decision model based on the so-called con rmation rules. Con rmation rules are generated separately for each diagnostic class so that selected rules cover (and should hence be able to reliably predict) a signi cant number of cases of the target class. At the same time, a con rmation rule should not cover the cases of non-target diagnostic classes, and ...