The phrase "a normal model with" is correct and usable in written English.
It can be used when describing a model that adheres to standard or typical characteristics in a specific context, such as statistics or machine learning.
Example: "In our analysis, we will use a normal model with a mean of zero and a standard deviation of one."
Alternatives: "a standard model with" or "a typical model featuring".
In technical language, we specified a normal model with an identity-link function and an exchangeable correlation structure.
The parametric model consists of a linkage analysis performed using a normal model with the Haley-Knott regression method implemented in the R/qtl package [ 52, 53].
For each phenotype and each expression trait, linkage analysis was performed using a normal model with the Haley-Knott regression method implemented in the R/qtl package (Broman et al. 2003).
We carried out a meta-analysis to estimate sensitivity and specificity of rapid tests, using a bivariate normal model with a complementary log-log link function.
Both a normal model and a diseased model with BO obstruction were developed for experimentation.
The different components of the reversible short-term disease activity were captured using a multivariate normal model with shifted means of the DAS28 variables to accurately reproduce the results of the PREMIER trial.
Further, the approximation BF ij ≈BF ij Schwarz does not require unrealistic amounts of data: a simple normal model with known SD has found the BIC approach to the BF excellent even for samples each of as few as five observations (Kass and Wasserman, 1995).
In simulation experiments, it has been shown that, in general, it is better to work with such a binomial model rather than with a normal model for transformed observed proportions [ 18].
Briefly, we first calculated QTL genotype probabilities, conditional on the available marker data ("calc.genoprob" function), and then used Haley-Knott regression ("scanone" function) to perform single-QTL genome scans with a normal model [60].
They have determined that additive and dominance QTL and residual variance estimates are much closer to the simulated true values when the data itself is heavy-tailed and the analysis is performed with the skewed Student's- t model rather than with a normal model.
A Bernoulli model was implemented to identify clusters of positive farms, followed by a Normal model to detect clustering of farms with high prevalence.
