Sentence examples for fixed regression from inspiring English sources

The number of simultaneously estimated parameters was 36 (2 × 13 period factors and 2 × 5 fixed regression coefficients).

For the fixed regression parameters, α, a suitable choice is the diffuse prior, i.e p∝constant.

The fixed regression coefficients β1 and β2 were assigned a vague Gaussian prior.

The model includes a fixed regression of phenotypes using SNP genotypes as a measure of the SNP effects.

β h is the coefficient of linear fixed regression on age at harvest (AGE h ) within the hth environment.

In the future, as more TD data become available electronically, the use of a fixed regression TDM could be a viable option.

Results were obtained by calculating SNP effects for a single replicate of the training data; FR-LS: fixed regression-least squares; RR-BLUP: random regression-BLUP; Bayes-R: Bayesian regression; SVR: support vector regression; PLSR: partial least squares regression.

The training data set included animals born before 1998; ASI: Australian Selection Index; PPT: protein percentage; FR-LS: fixed regression-least squares; RR-BLUP: random regression-BLUP; Bayes-R: Bayesian regression; SVR: support vector regression; PLSR: partial least squares regression.

ASI: Australian Selection Index; PPT: protein percentage; FR-LS: fixed regression-least squares; RR-BLUP: random regression-BLUP; Bayes-R: Bayesian regression; SVR: support vector regression; PLSR: partial least squares regression.

Further variance analyses were performed by adding fixed regressions for the first 1, 2, 3, and 4 PCs to the equation shown in formula (2).

A quadratic polynomial in days on test with fixed regressions for sex and batch, random regressions for additive genetic, pen, litter and individual permanent environmental effects was used, with two different models for the residual variance: constant in model 1 and modelled with a quadratic polynomial depending on the day on test d m as follows in model 2:.