The objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects ...

Milk yield records (305d, 2X, actual milk yield) of 123,639 registered first lactation Holstein cows were used to compare linear regression (y = β(0) + β(1)X + e), quadratic regression, (y = β(0) + β(1)X + β(2)X(2) + e) cubic regression (y = β(0) + β(1)X + β(2)X(2) + β(3)X(3) +e) and fixed factor models, with cubic-spline interpolation models, for estimating the effects of inbreeding on milk yi...

In spline regression models, the smoothness of the tted model depends on the knots via their location and how many knots there are. An alternative to knot selection is to keep all the knots, but to restrict their in uence on the tted model, that is, to constrain the values of these coe cients which correspond to the spline basis functions. For example, we can bound the squared L2 norm by some c...

A new algorithm for Computer Aided Geometric Design of least squares (LS) splines with variable knots, named GeDS, is presented. It is based on interpreting functional spline regression as a parametric B-spline curve, and on using the shape preserving property of its control polygon. The GeDS algorithm includes two major stages. For the first stage, an automatic adaptive, knot location algorith...

We quantified the potential increase in accuracy of expected breeding value for weights of Nelore cattle, from birth to mature age, using multi-trait and random regression models on Legendre polynomials and B-spline functions. A total of 87,712 weight records from 8144 females were used, recorded every three months from birth to mature age from the Nelore Brazil Program. For random regression a...

Linear regression is an analytic approach commonly used in public health to examine the relationship between continuous dependent (e.g., blood pressure) and independent (e.g., body mass index (BMI)) variables. For any regression procedure it is desirable to use models that closely fit the data. Transformations of the response variable can improve the fit and may correct violations of model assu...

This paper presents a fully Bayesian approach to regression splines with automatic knot selection in generalized semiparametric models for fundamentally non Gaussian responses In a basis function representation of the regression spline we use a B spline basis The reversible jump Markov chain Monte Carlo method allows for simultaneous estimation both of the number of knots and the knot placement...

Penalized spline criteria involve the function of goodness of fit and penalty, which in the penalty function contains smoothing parameters. It serves to control the smoothness of the curve that works simultaneously with point knots and spline degree. The regression function with two predictors in the non-parametric model will have two different non-parametric regression functions. Therefore, we...