David Siegmund is affiliated with Stanford University in the United States and specializes primarily in the field of Mathematics, with a focus on Statistics and Probability. Their research extends into subfields including Artificial Intelligence, Genetics, Finance, and General Health Professions.
The main topics that characterize Siegmund's work comprise Statistical Methods and Inference, Bayesian Methods and Mixture Models, and Financial Risk and Volatility Modeling. Additional topics of interest include Gene expression and cancer classification, Genetic Mapping and Diversity in Plants and Animals, as well as Genetic and phenotypic traits in livestock and Genetics and Plant Breeding.
Siegmund's recent publications include:
Frequent co-authors collaborating with Siegmund include Xiao Fang, Jian Li, Jonas Wallin, Małgorzata Bogdan, and Piotr Szulc. The most common venues for Siegmund's publications are arXiv (Cornell University), The Annals of Statistics, Genetics, Centre International de Rencontres Mathématiques, and Chemie Ingenieur Technik.
Throughout their career, Siegmund has received several honors: Member of the National Academy of Sciences (2002), Samuel S. Wilks Memorial Award from the American Statistical Association (1998), Fellowship of the American Academy of Arts and Sciences (1994), Wald Memorial Lecturer (1984), and Fellowship of the John Simon Guggenheim Memorial Foundation (1974).
John D. Storey;Jonathan E. Taylor;David Siegmund
Yuan Shih Chow;Herbert Ellis Robbins;David Siegmund
David Siegmund
Rupert Miller;David Siegmund
Unknown
Nancy R. Zhang;David O. Siegmund
D. Siegmund;E. S. Venkatraman
Josée Dupuis;David Siegmund
Unknown
E Feingold;P O Brown;D Siegmund
David Siegmund
Barry James;Kang Ling James;David Siegmund
Hyune Ju Kim;David Siegmund
Yao Xie;David Siegmund
Edward G. Carlstein;Hans-Georg Müller;David Siegmund
Moshe Pollak;David Siegmund
Unknown
David O. Siegmund;Keith J. Worsley
D. Siegmund
Herbert Robbins;David Siegmund;David Siegmund
D. Siegmund
Nancy R. Zhang;David O. Siegmund;Hanlee Ji;Jun Z. Li
T. L. Lai;Bruce Levin;Herbert Robbins;David Siegmund
If you think any of the details on this page are incorrect, let us know.
For students studying Mathematics in the USA, exploring related online degrees can open diverse career opportunities. Many graduates leverage their quantitative skills to pursue advanced degrees in fields like data science, business analytics, and finance.
If you're interested in combining math expertise with business acumen, consider some of the online MBA programs that accept transfer credits. These programs offer flexibility and can accelerate your path to leadership roles without redundant coursework.
For those more focused on data-driven decision-making, the best masters in data analytics programs help sharpen skills in statistical modeling, machine learning, and big data tools.
If ease of admission and program accessibility are priorities, researching the easiest MBA to get into can provide options with less competitive entry requirements.
Additionally, many students find value in investigating easy online MBA programs that balance quality and convenience for working professionals seeking career advancement.
University of Montreal
Hanyang University
National Taiwan University
University of Warwick
University of Glasgow
San Diego State University
University of Brescia
University of Cambridge
University of Michigan–Ann Arbor
China University of Geosciences
University of Münster
Emory University
TU Dresden
University of Barcelona
University of Pennsylvania
University of Dundee
