A quasi-independent (QI) subsequence is a subset of time-series observations obtained by systematic sampling. Because the observations appear to be independent, as determined by the runs tests, classical statistical techniques can be used on those observations directly. This paper discusses implementation of a sequential procedure to determine the simulation run length to obtain a QI subsequenc...

The problem of f -divergence estimation is important in the fields of machine learning, information theory, and statistics. While several nonparametric divergence estimators exist, relatively few have known convergence properties. In particular, even for those estimators whose MSE convergence rates are known, the asymptotic distributions are unknown. We establish the asymptotic normality of a r...

Complex objects are often described by multiple representations modeling various aspects and using various feature transformations. To integrate all information into classification, the common way is to train a classifier on each representation and combine the results based on the local class probabilities. In this paper, we derive so-called confidence estimates for each of the classifiers refl...

BACKGROUND Roughly 25 years ago, the United States National Institute on Drug Abuse (US NIDA) initiated the creation of public use datasets for its National Household Survey on Drug Abuse, since re-named the National Survey on Drug Use and Health (NSDUH). The Substance Abuse and Mental Health Services Administration (SAMHSA), which assumed responsibility for the survey in 1992, has continued an...

Uniform confidence bands for densities f via nonparametric kernel estimates were first constructed by Bickel and Rosenblatt [Ann. Statist. 1, 1071–1095]. In this paper this is extended to confidence bands in the deconvolution problem g = f ∗ ψ for an ordinary smooth error density ψ. Under certain regularity conditions, we obtain asymptotic uniform confidence bands based on the asymptotic distri...

This paper addresses the problem of deriving measures that express the degree of the reliability of motion vectors estimated by a Block Matching motion estimation method. We express the block matching motion estimation scheme in the probabilistic framework and derive the confidence measures in terms of the a-posteriori probabilities of the estimated vectors. The type of the underlying condition...

We address the task of automatically predicting if summarization system performance will be good or bad based on features derived directly from either singleor multi-document inputs. Our labelled corpus for the task is composed of data from large scale evaluations completed over the span of several years. The variation of data between years allows for a comprehensive analysis of the robustness ...