Log-normal Error Models
Log-Normal Error Models is a statistical error modeling framework that belongs to the class of multiplicative error models, distinguished by its assumption that the residuals follow a log-normal distribution, thereby ensuring positive predicted values and accommodating heteroscedasticity commonly observed in biological and pharmacokinetic data.
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2017–2026
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Relative location estimation in wireless sensor networks
Neal Patwari, Alfred O. Hero, Matt Perkins et al. · IEEE Transactions on Signal Processing · 2003 · 1.9K citations
PBSIM: PacBio reads simulator—toward accurate genome assembly
Yukiteru Ono, Kiyoshi Asai, Michiaki Hamada · Bioinformatics · 2012 · 401 citations · Full text
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Indiana University – Purdue University Indianapolis, United States
2 publications · 161 citations
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École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
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8 publications
277 citations
Memorial University of Newfoundland
St. John's, Canada
3 h-index
9 publications
827 citations
University of Iowa
Iowa City, United States
3 h-index
3 publications
251 citations

Kwame Nkrumah University of Science and Technology
Kumasi, Ghana
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2 publications
51 citations
The University of Texas at Austin
Austin, United States
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2 publications
237 citations
New York University
New York, United States
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2 publications
175 citations

Lawrence Berkeley National Laboratory
Berkeley, United States
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28 citations
Massachusetts Institute of Technology
Cambridge, United States
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3 publications
613 citations
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