David Siegmund, who holds the John D. and Sigrid Banks Chair at Stanford University, Stanford, CA, is a statistician who is comfortable in both the airy heights of theory and the practicalities of real-world applications. He works at the interface between probability and statistics, applying the tools he develops to topics as diverse as the design of medical clinical trials and mapping the locations of genes that are involved in specific physiological traits.
His work has earned him several awards, including a Guggenheim Fellowship in 1974, the Humboldt Prize in 1980, and membership in the American Academy of Arts and Sciences in 1994. In 2002 he was elected to the National Academy of Sciences. His Inaugural Article (1), published in this issue of PNAS, reviews recent methodological developments in quantitative trait locus mapping and addresses the problem of mapping with selected, rather than random, samples.
[ More details on PNAS ].
Presentation Topic: Peak Detection and Model Selection in Statistical Genetics
High throughput methods in genetics/molecular biology produce profiles indexed by position in a genome that are punctuated by signals in the form of "peaks"â of known or unknown shape. Statistical problems involve detection of peaks and estimation of their location and amplitude, while accounting appropriately for issues of multiple comparisons. In this talk I survey a number of these problems and suggest solutions based on multiple hypothesis testing, model selection via the Bayes Information Criterion (BIC), or a
combination of both.
This is joint research with Nancy Zhang and Benny Yakir.