The Future of Indirect Evidence
Reads0
Chats0
TLDR
This article is basically the text of a recent talk featuring some examples from current practice, with a little bit of futuristic speculation, where indirect evidence seems too important to ignore.Abstract:
Familiar statistical tests and estimates are obtained by the direct observation of cases of interest: a clinical trial of a new drug, for instance, will compare the drug’s effects on a relevant set of patients and controls. Sometimes, though, indirect evidence may be temptingly available, perhaps the results of previous trials on closely related drugs. Very roughly speaking, the difference between direct and indirect statistical evidence marks the boundary between frequentist and Bayesian thinking. Twentieth-century statistical practice focused heavily on direct evidence, on the grounds of superior objectivity. Now, however, new scientific devices such as microarrays routinely produce enormous data sets involving thousands of related situations, where indirect evidence seems too important to ignore. Empirical Bayes methodology offers an attractive direct/indirect compromise. There is already some evidence of a shift toward a less rigid standard of statistical objectivity that allows better use of indirect evidence. This article is basically the text of a recent talk featuring some examples from current practice, with a little bit of futuristic speculation.read more
Citations
More filters
Journal ArticleDOI
Estimation of COVID-19 spread curves integrating global data and borrowing information
TL;DR: A Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries outperforms an existing individual country-based model and provides a powerful predictive tool endowed with uncertainty quantification.
Journal ArticleDOI
Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists.
Hao-Ting Wang,Hao-Ting Wang,Jonathan Smallwood,Janaina Mourao-Miranda,Cedric Huchuan Xia,Theodore D. Satterthwaite,Danielle S. Bassett,Danilo Bzdok +7 more
TL;DR: Canonical correlation analysis is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities and so is well suited to the analysis of big neuroscience datasets.
Journal ArticleDOI
Inferring multiple graphical structures
TL;DR: This paper proposes two approaches for estimating multiple related graphs, by rendering the closeness assumption into an empirical prior or group penalties, and provides quantitative results demonstrating the benefits of the proposed approaches.
References
More filters
Journal ArticleDOI
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini,Yosef Hochberg +1 more
TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Journal ArticleDOI
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.
Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Book
Simultaneous Statistical Inference
TL;DR: In this article, the authors presented a case of two means regression method for the family error rate, which was used to estimate the probability of a family having a nonzero family error.
Journal ArticleDOI
Estimation of the Mean of a Multivariate Normal Distribution
TL;DR: In this article, an unbiased estimate of risk is obtained for an arbitrary estimate, and certain special classes of estimates are then discussed, such as smoothing by using moving averages and trimmed analogs of the James-Stein estimate.
Related Papers (5)
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini,Yosef Hochberg +1 more