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However, it is well known that several phenomena are not always represented by the normal model due to lack of symmetry or the presence of heavy- and light-tailed distributions related to the normal law in the data.
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It explicitly shows that only in very few cases the accuracies of the proposed method are lower than the ones obtained by the normal ARMA model.
While such heterogeneity seems plausible, if not altogether obvious, it may not be adequately expressed by either the contaminated normal model or the 2-component model.
We begin by introducing the general normal model for BRMA and discuss analytically why the between-study correlation can be estimated at the edge of its parameter space.
Clearly, the background as estimated by the exponential-normal model does not have a normal distribution, since the QQ plot does not display a straight line.
The peak discharge is estimated properly also by the log-normal model.
Such propagation characteristics have also been discussed recently within the 3GPP [41, 56] and cannot be reproduced by the log-normal model.
The performance of all the models except the normal and Gumbel is satisfactory in predicting the peak discharge, time to peak, and the UH shape for storm C. The normal and Gumbel distributions are also not successful in predicting the time to peak and the rising limb of the UH for storm D. The peak discharge is estimated with less error by the log-normal model.
Given that 6-month follow-up assessments were missing in 17 (23%; 7 BB, 10 CC) individuals, multiple imputation with data augmentation under the multivariate normal model by PROC MI (SAS Institute, Cary, NC) was performed.
A Bernoulli model was implemented to identify clusters of positive farms, followed by a Normal model to detect clustering of farms with high prevalence.
The multivariate normal model was introduced by Rubin (1987; see also Little & Rubin 2002).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com