Health-behavior intervention studies have focused primarily on comparing a new program over the control using randomized controlled trials. However, we are seeing a dramatic increase in the number of possible components (factors) due to developments in science and technology (internet, web-based surveys, and so on). These changes dictate the need for alternative methods that can screen a large set of potentially important components in order to identify the important ones quickly and economically. We have developed and implemented a multiphase experimentation strategy for accomplishing this goal. This article describes the screening phase of this strategy and the use of fractional factorial designs for studying several factors (also called components) economically and uses two on-going projects on behavioral intervention to illustrate their usefulness. The fractional factorial designs are supplemented with follow up experiments in the refining phase, so any critical assumptions about interactions can be verified and the dosage levels can be optimized.
Financial crimes affect millions of people every year and financial institutions must employ methods to protect themselves and their customers. The use of statistical methods to address these problems faces many challenges. Financial crimes are rare events that lead to extreme class imbalances. Criminals deliberately attempt to conceal the nature of their actions and quickly change their strategies over time, resulting in class overlap and concept drift. In some cases, legal constraints and investigation delays make it impossible to actually verify suspected crimes in a timely manner, resulting in class mislabeling or unknown labels. In addition, the volume and complexity of financial data require algorithms to be not only effective, but also efficiently trained and executed. This article focuses on two important types of financial crimes: fraud and money laundering. It discusses some of the traditional statistical techniques that have been applied as well as more recent machine learning and data mining algorithms. The goal of the article is to introduce the subject and to provide a survey of broad classes of methodologies accompanied by selected illustrative examples.
This paper aims to generalize and unify classical criteria for comparisons of
balanced lattice designs, including fractional factorial designs,
supersaturated designs and uniform designs. We present a general majorization
framework for assessing designs, which includes a stringent criterion of
majorization via pairwise coincidences and flexible surrogates via convex
functions. Classical orthogonality, aberration and uniformity criteria are
unified by choosing combinatorial and exponential kernels. A construction
method is also sketched out.Comment: Published at http://dx.doi.org/10.1214/009053605000000679 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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