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The modeling and analysis of computer communications networks give rise to a variety of interesting statistical problems. This paper focuses on network tomog-raphy, a term used to characterize two classes of large-scale inverse problems. The first deals with passive tomography where aggregate data are collected at the individual router/node level and the(More)
BACKGROUND Although the optimization of behavioral interventions offers the potential of both public health and research benefits, currently there is no widely agreed-upon principled procedure for accomplishing this. PURPOSE This article suggests a multiphase optimization strategy (MOST) for achieving the dual goals of program optimization and program(More)
Health behavior intervention studies have focused primarily on comparing new programs and existing programs via randomized controlled trials. However, numbers of possible components (factors) are increasing dramatically as a result of developments in science and technology (e.g., Web-based surveys). These changes dictate the need for alternative methods(More)
— There is increased interest among network administrators and service providers to estimate quality of service parameters associated with their network operations. Two approaches are currently used for collecting and modeling network data: passive monitoring of link-level data or active probing to obtain path-level measurements. In this paper, we(More)
There has been considerable interest over the last few years in collecting and analyzing internet traffic data in order to estimate quality of service parameters such as packet loss rates and delay distributions. In this paper, we focus on fast and efficient estimation methods for network link delay distributions based on end-to-end measurements obtained by(More)
Identifying the spatio-temporal network structure of brain activity from multi-neuronal data streams is one of the biggest challenges in neuroscience. Repeating patterns of precisely timed activity across a group of neurons is potentially indicative of a microcircuit in the underlying neural tissue. Frequent episode discovery, a temporal data mining(More)
Repeating patterns of precisely timed activity across a group of neurons (called frequent episodes) are indicative of networks in the underlying neural tissue. This letter develops statistical methods to determine functional connectivity among neurons based on nonoverlapping occurrences of episodes. We study the distribution of episode counts and develop a(More)
The multi-phase optimization strategy (MOST) is an experimental framework for achieving the goals of developing, optimizing and evaluating multi-component behav-ioral interventions. MOST was proposed by Collins et al. [8] as a precursor to a randomized confirmatory trial. MOST consists of three ordered phases: screening, refining, and confirming; and it(More)