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Institution

University of Connecticut

EducationStorrs, Connecticut, United States
About: University of Connecticut is a education organization based out in Storrs, Connecticut, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 35297 authors who have published 81224 publications receiving 2952682 citations. The organization is also known as: UConn & Storrs Agricultural School.


Papers
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Journal ArticleDOI
TL;DR: Statin-associated symptoms are important because they prompt dose reduction or discontinuation of these life-saving mediations, and management of SAS requires making the possible diagnosis, altering or discontinuing the statin treatment, and using alternative lipid-lowering therapy.

474 citations

Journal ArticleDOI
TL;DR: It is concluded that there is insufficient evidence to prove the etiologic role of CoQ10 deficiency in statin-associated myopathy and that large, well-designed clinical trials are required to address this issue.

474 citations

Journal ArticleDOI
TL;DR: This work generalized an existing scale, the Labeled Magnitude Scale (LMS), by placing the label "strongest imaginable sensation of any kind" at the top, and produced similar results suggesting that the gLMS is valid for taste comparisons across nontasters, medium tasters, and supertasters.

474 citations

Book
27 Aug 2014
TL;DR: This book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains and explains how the theoretical aspects influence the hypothesisTesting and changepoint Detection problems as well as the design of algorithms.
Abstract: Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The authors not only emphasize traditional binary hypotheses but also substantially more difficult multiple decision problems. They address scenarios with simple hypotheses and more realistic cases of two and finitely many composite hypotheses. The book primarily focuses on practical discrete-time models, with certain continuous-time models also examined when general results can be obtained very similarly in both cases. It treats both conventional i.i.d. and general non-i.i.d. stochastic models in detail, including Markov, hidden Markov, state-space, regression, and autoregression models. Rigorous proofs are given for the most important results. Written by leading authorities in the field, this book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains. It explains how the theoretical aspects influence the hypothesis testing and changepoint detection problems as well as the design of algorithms.

474 citations


Authors

Showing all 35666 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Richard A. Flavell2311328205119
Ralph Weissleder1841160142508
Eric J. Nestler178748116947
David L. Kaplan1771944146082
Masayuki Yamamoto1711576123028
Mark Gerstein168751149578
Marc A. Pfeffer166765133043
Carl W. Cotman165809105323
Murray F. Brennan16192597087
Alfred L. Goldberg15647488296
Xiang Zhang1541733117576
Hakon Hakonarson152968101604
Christopher P. Cannon1511118108906
James M. Wilson150101078686
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023129
2022552
20214,491
20204,342
20193,789
20183,498