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Most articles are motivated by applications arising from medicine analyzing neuroimages, comparing treatments, inferring optimal individualized treatment rules. The others address challenging theoretical questions. They shed light on delicate theoretical problems, offer guidance for better practice, open exciting new territories to explore.

  • A Guide to HVAC Building Services Calculations.
  • The Big Fix;
  • Medical Challenges for the New Millennium: An Interdisciplinary Task?

We hope you will enjoy perusing the special issue and that it will serve as a useful pivot towards methods that can address new challenges in data science. Export Citation.

Statistical Science

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Show Summary Details. More options …. The International Journal of Biostatistics. See all formats and pricing. Online ISSN See all formats and pricing Online. Prices are subject to change without notice.

By Robert E. Kass, and Comments by Andrew Gelman

Prices do not include postage and handling if applicable. Volume 14 Issue 2 Nov Issue 1 May Volume 13 Issue 2 Nov Issue 1 May Volume 12 Issue 2 Nov Issue 1 May , pp.

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Volume 11 Issue 2 Nov , pp. Volume 10 Issue 2 Nov , pp.

Generalizability in Causal Inference: Theory and Algorithms

Volume 9 Issue 2 Nov , pp. Volume 8 Issue 2 Jan , pp.

Volume 7 Issue 1 Jan , pp. Volume 6 Issue 2 Jan Causal Inference. Volume 5 Issue 1 Jan Students unfamiliar with numerical computation in any language on any platform should consult with the instructor before registering for the course.

Unit 4A: Introduction to Statistical Inference

Lab Home Prof. Aaron A. King, Ph. Nelson G.

Kass : Statistical Inference: The Big Picture

EEB Model-based Statistical Inference for Ecology. Students completing the course will have gained facility with the statistical analyses most commonly needed in ecology, good habits of data organization, visualization, and analysis, which will facilitate their ability to perform sound, transparent, and reproducible research, a deep understanding of the fundamental theory of statistical inference, which will allow the design of new analyses customized to specific questions and data, and the background needed to understand and criticize models and statistical inference in the scientific literature.

The course syllabus can be found here.