This paper addresses the problem of approximate merging of two adjacent B-spline curves into one B-spline curve. The basic idea of the approach is to find the conditions for precise merging of two B-spline curves, and perturb the control points of the curves by constrained optimization subject to satisfying these conditions. To obtain a merged curve without superfluous knots, we present a new k...

This work relates the framework of model-based clustering for spatial functional data where the data are surfaces. We first introduce a Bayesian spatial spline regression model with mixed-effects (BSSR) for modeling spatial function data. The BSSR model is based on Nodal basis functions for spatial regression and accommodates both common mean behavior for the data through a fixed-effects part, ...

The computation of the cumulative distribution (cdf), the complementary cdf (ccdf), and the density of certain shot-noise random variables is discussed. After subtracting oo a few terms that can be computed in closed form, what remains can be approximated by a general method for approximating samples of a cdf or ccdf by summing a Fourier series whose coeecients are modulated samples of their ch...

Spline knots based on Bézier curves or B-splines can exhibit a knot type that is different from that exhibited by its control polygon, i.e., the spline and its control polygon are not ambient isotopic. By forming composite knots from suitably designed building blocks the difference in knottedness of the two 1-manifolds can be made arbitrarily large.

In this paper, a new methodology is developed for knots placement for cubic spline approximation, using Simulated Annealing. It is not necessary to convert the problem into a discrete combinatorial optimization problem, as presented in other paradigms inspired in genetic algorithm or artificial immune systems, and therefore, removing the constrain of combinatorial optimization problem in the pr...

In order to obtain a good spline model from large measurement data, we frequently have to deal with knots as variables, which becomes a continuous, non-linear and multivariate optimization problem with many local optima. Hence, it is very difficult to obtain a global optima. In this paper, we present a method to convert the original problem into a discrete combinatorial optimization problem and...

Genetic parameters were estimated with restricted maximum likelihood for individual test-day milk, fat, and protein yields and somatic cell scores with a random regression cubic spline model. Test-day records of Holstein cows that calved from 1994 through early 1999 were obtained from Dairy Records Management Systems in Raleigh, North Carolina, for the analysis. Estimates of heritability for in...