Don X. Sun

Internet Traffic Tends Toward Poisson and Independent as the Load Increases

Network devices put packets on an Internet link, and multiplex, or superpose, the packets from different active connections.

Detecting fraud in the real world

Finding telecommunications fraud in masses of call records is more difficult than finding a needle in a haystack. In the haystack problem, there is only one needle that does not look like hay, the pieces of hay all look similar, and neither the needle nor the hay changes much over time. Fraudulent calls may be rare like needles in haystacks, but they are much more challenging to find. Callers are dissimilar, so calls that look like fraud for one account look like expected behavior for another, while all needles look the same. Moreover, fraud has to be found repeatedly, as fast as fraud calls are placed, the nature of fraud changes over time, the extent of fraud is unknown in advance, and fraud may be spread over more than one type of service. For example, calls placed on a stolen wireless telephone may be charged to a stolen credit card. Finding fraud is like finding a needle in a haystack only in the sense of sifting through masses of data to find something rare. This chapter describes some issues involved in creating tools for building fraud systems that are accurate, able to adapt to changing legitimate and fraudulent behavior, and easy to use.

Optimal Blocking Schemes for 2n and 2n—p Designs

Systematic sources of variations in factorial experiments can be effectively reduced without biasing the estimates of the treatment effects by grouping the runs into blocks. For full factorial designs, optimal blocking schemes are obtained by applying the minimum aberration criterion to the block defining contrast subgroup. A related concept of order of estimability is proposed. For fractional factorial designs, because of the intrinsic difference between treatment factors and block variables, the minimum aberration approach has to be modified. A concept of admissible blocking schemes is proposed for selecting block designs based on multiple criteria. The resulting 2 n and 2 n–P designs are shown to have better overall properties for practical experiments than those in the literature.

Analysis of Interval-Censored Data From Fractionated Experiments Using Covariance Adjustment

Censored data are commonly observed in industrial experiments such as for life testing and reliability improvement. Analyzing censored data from highly fractionated experiments presents a challenging problem to experimenters because many traditional methods become inadequate. Motivated by the data from a fluorescent-lamp experiment, we consider in this article analyzing censored data from highly fractionated experiments using covariance adjustment based on multivariate multiple regression models, which make use of the joint distribution of multivariate response variables. The Bayesian approach is taken for the main statistical inference. The posterior distribution of the parameters is obtained using the data augmentation algorithm. We illustrate the methodology with the fluorescent-lamp experiment data. With the real example and a simulation study, we show that covariance adjustment can lead to both dramatic variance reduction and possible bias reduction.