A new multivariate Archimedean copula estimation method is proposed in a non-parametric setting. The method uses the so called Geometrically Designed splines (GeD splines), recently introduced by Kaishev et al. (2006 a,b) [10] and [11], to represent the cdf of a random variable Wθ, obtained through the probability integral transform of an Archimedean copula with parameter θ. Sufficient conditio...

Abstract Background Generalized linear mixed models (GLMMs), typically used for analyzing correlated data, can also be smoothing by considering the knot coefficients from a regression spline as random effects. The resulting are called semiparametric (SPMMs). Allowing to follow normal distribution with mean zero and constant variance is equivalent using penalized ridge type penalty. We introduce...

Flexible discriminant analysis (FDA) is a general methodology which aims at providing tools for multigroup non linear classification. It consists in a nonparametric version of discriminant analysis by replacing linear regression by any nonparametric regression method. A new option for FDA, consisting in a nonparametric regression method based on B-spline functions, will be introduced. The relev...

We construct a polynomial spline similar to the box spline, except that we relax the condition that its knots are spaced uniformly in each direction. The recurrence relation for this spline is a generalization of the recurrence relations for the box spline and univariate B-spline. The construction generalizes to exponential-polynomial splines [5]. §

Motivated by the problem of classifying hormone trajectories, this article proposes a flexible semiparametric Bayesian methodology for hierarchical functional data. The approach is based on a hierarchical spline model, with the number and location of knots and the distribution of the random spline coefficients treated as unknown. Assuming a discrete distribution for the spline coefficients, we ...