Archive
About the Statomics Lab
The Complexity and Statistics Research Lab was called “The Statomics Lab” until 4 February 2017. The word statomics abbreviates statistical inference and computation in genomics. David Bickel launched the lab in June of 2007 at the Ottawa Institute of Systems Biology.
Mathematical Reviews
Full text of David Bickel’s reviews
Reviews of David Bickel’s mathematical publications
(MR subscription needed)
Meeting: information theory & statistics
The Fourth Workshop on Information Theoretic Methods in Science and Engineering
7-10 August 2011, Helsinki, Finland
Local false discovery rate
Efron clearly explains important recent advances in estimating the local false discovery rate. He also contrasts the methodology with more conventional multiple comparison procedures.
Statomics on Web 2.0
Follow the Statomics Lab on Facebook.
Was the loss function a mistake?
Laplace’s “introduction of a loss function proved to be a serious mistake, which came to hamper the development of an objective theory of statistical inference to the present day” (Hald, 2007, pp. 3-4).
Fisherian alternatives to conventional statistics
Novel developments in statistics and information theory call for a reconsideration of important aspects of two of R. A. Fisher’s most controversial ideas: the fiducial argument and the direct use of the likelihood function. Some key features of observed confidence levels, the direct use of the likelihood function, and the minimum description length principle are summarized here:
- Like the fiducial distribution, a probability measure of observed confidence levels is in effect a posterior probability distribution of the parameter of interest that does not require any prior distribution. Derived from sets of confidence intervals, this probability distribution of a parameter of interest is traditionally known as a confidence distribution. When the parameter of interest is scalar, the observed confidence level of a composite hypothesis is equal to its fiducial probability. On the other hand, observed conference levels do not suffer from the difficulties of constructing a fiducial distribution of a vector parameter.
- The likelihood ratio serves not only as a tool for the construction of point estimators, p-values, confidence intervals, and posterior probabilities, but is also fruitfully interpreted as a measure of the strength of statistical evidence for one hypothesis over another through the lens of a family of distributions. Modern versions of Fisher’s evidential use of the likelihood overcome multiplicity problems that arise in standard frequentism without resorting to a prior distribution.
- A related approach is to select the family of distributions using a modern information-theoretic reinterpretation of the likelihood function. In particular, the minimum description length principle extends the scope of Fisherian likelihood inference to the challenging problem of model selection.
Information Theoretic Methods
2010 Workshop on Information Theoretic Methods in Science and Engineering
August 16 – 18, 2010 | Tampere, Finland
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