Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce fraud, fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the detection of fraud are essential if we are to catch fraudsters once fraud prevention has failed. Statistics and machine learning provide effective technologies for fraud detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card fraud, telecommunications fraud and computer intrusion, to name but a few. We describe the tools available for statistical fraud detection and the areas in which fraud detection technologies are most used.

We discuss various technical problems that have arisen in attempting to implement the Telecommunications Act of 1996, the purpose of which was to ensure fair competition in the local telecommunications market. We treat the interpretation of the "parity'' requirement, testing the parity hypothesis, the effect of correlation, disaggregation and reaggregation, "balancing,'' benchmarks, payment schedules and some computational problems. Also we discuss the difficulty of working in an adversarial (rather than scientific) environment.

By slightly reframing the concept of covariance adjustment in randomized experiments, a method of exact permutation inference is derived that is entirely free of distributional assumptions and uses the random assignment of treatments as the "reasoned basis for inference.'' This method of exact permutation inference may be used with many forms of covariance adjustment, including robust regression and locally weighted smoothers. The method is then generalized to observational studies where treatments were not randomly assigned, so that sensitivity to hidden biases must be examined. Adjustments using an instrumental variable are also discussed. The methods are illustrated using data from two observational studies.

The mixture transition distribution model (MTD) was introduced in 1985 by Raftery for the modeling of high-order Markov chains with a finite state space. Since then it has been generalized and successfully applied to a range of situations, including the analysis of wind directions, DNA sequences and social behavior. Here we review the MTD model and the developments since 1985. We first introduce the basic principle and then we present several extensions, including general state spaces and spatial statistics. Following that, we review methods for estimating the model parameters. Finally, a review of different types of applications shows the practical interest of the MTD model.

Emanuel Parzen was born in New York City on April 21, 1929. He attended the Bronx High School of Science, received an A.B. in Mathematics from Harvard University in 1949, an M.A. in Mathematics from the University of California at Berkeley in 1951 and his Ph.D. in Mathematics and Statistics in 1953, also at Berkeley. He was a research scientist at Hudson Labs, Physics Department of Columbia University, from1953 to1956 and an Assistant Professor of Mathematical Statistics at Columbia from1955 to1956. In1956, he moved to Stanford University, where he stayed until1970, at which time he joined the faculty at the State University of New York at Buffalo, where he served first as Leading Professor and Chairman of the Department of Statistics and then as Director of Statistical Science. In1978 he moved to Texas A&M University as a Distinguished Professor, a post he currently holds. He has been a Fellow at Imperial College London, at IBM Systems Research Institute and at the Center for Advanced Study in the Behavioral Sciences at Stanford, as well as a Visiting Professor at the Sloan School of MIT, the Department of Statistics at Harvard and the Department of Biostatistics at Harvard. In 1959 he married Carol Tenowitz. They have two children and four grandchildren.

Professor Parzen has authored or coauthored over 100 papers and 6 books. He has served on innumerable editorial boards and national committees, and has organized several influential conferences and workshops. He has directed the research of many graduate students and provided advice, encouragement and collaboration to students and colleagues around the world. To honor these contributions, he has been elected a Fellow of the American Statistical Association, of the Institute of Mathematical Statistics and of the American Association for the Advancement of Science. In 1994, he was awarded the prestigious Samuel S. Wilks Memorial Medal by the American Statistical Association.