A log‐sinh transformation for data normalization and variance stabilization
By Quan J. Wang, Durga L. Shrestha, David E. Robertson, P. Pokhrel. 2012
158 citations recorded by the source corpus.
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/7fec58eb450e45301fa032a3e64f953c0d2f44fb
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
- George A. Kuczera
- Mark A. Thyer
- David McInerney
- Julien Lerat
- Dmitri Kavetski
- Jasper A. Vrugt
- M. Clark
In Global and Planetary Change
- Dmitri Kavetski
- George A. Kuczera
- Mark A. Thyer
- Jasper A. Vrugt
- David McInerney
- Julien Lerat
- M. Clark
- Keith Beven
In Water Science and Technology
- Dmitri Kavetski
- George A. Kuczera
- Mark A. Thyer
- Jasper A. Vrugt
- David McInerney
- Julien Lerat
- M. Clark
- Keith Beven
Citations in the corpus
- Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors (2017, methodology): “It is insightful to recognize that parameter B controls the values of Q over which the log-sinh variance-stabilizing function slshðQ; A; BÞ5Btanh Q1AB tapers off [see Wang et al., 2012, Table 3 and Figure 2].”