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.

100

Publications

7.6K

Citations

232

Authors

135

Institutions

Publications per year

2017–2026

14

Authors

232

  1. GC
    George C. Runger

    Arizona State University, United States

    2 publications · 237 citations

    2

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  2. WT
    Wanzhu Tu

    Indiana University – Purdue University Indianapolis, United States

    2 publications · 161 citations

    2

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  3. DH
    David H. Young

    St. Joseph's Hospital and Medical Center, United States

    2 publications · 73 citations

    2

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  4. DV
    D. V. Hinkley

    The University of Texas at Austin, United States

    2 publications · 237 citations

    2

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  5. BL
    Brian L. Wiens

    Amgen (United States), United States

    2 publications · 104 citations

    2

    h-index

1–5 of 232

Venues

  1. 6

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  2. Biometrics

    3 publications · 359 citations

    3

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  3. Journal of Applied Physics

    2 publications · 38 citations

    2

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  4. Technometrics

    2 publications · 73 citations

    2

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  5. 2

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1–5