If a model is fitted to empirical data, bias can arise from terms which are not incorporated in the model assumptions. As a consequence the commonly used optimality criteria based on the generalized variance of the estimate of the model parameters may not lead to efficient designs for the statistical analysis. In this note some general aspects of all-bias designs are presented, which were intro...

Regression spline smoothing is a popular approach for conducting nonparametric regression. An important issue associated with it is the choice of a ‘‘theoretically best’’ set of knots. Different statistical model selection methods, such as Akaike’s information criterion and generalized cross-validation, have been applied to derive different ‘‘theoretically best’’ sets of knots. Typically these ...

The Correlation Power Analysis (CPA) is probably the most used side-channel attack because it seems to fit the power model of most standard CMOS devices and is very efficiently computed. However, the Pearson correlation coefficient used in the CPA measures only linear statistical dependences where the Mutual Information (MI) takes into account both linear and nonlinear dependences. Even if ther...

The aim of this study is to determine the effectiveness of a natural tropical tree resin in controlling termites thus providing protection from their destruction. Tree resin from the bark of tropical trees offers the potential for this protection. Termites were fed by filter paper soaked in tropical tree resin dissolved in a solvent at different concentrations for 15 days. The number of termite...

In this paper, a generalised spline filter, that has a unified description for both the linear spline filter and the nonlinear robust spline filter, is proposed. Based on the M-estimation theory, the general spline filter model can be solved by using an Iterated Reweighted Least Squared method which is also general for both the linear and nonlinear spline filter. The algorithm has been verified...

in many statistical process control applications, the quality of a process is characterized by a profile. a profile is a function in terms of one or more explanatory variables. in profile monitoring, one is interested to monitor the performance of a process or product using this functional relationship. control charts for monitoring nonparametric profiles are useful when the relationship is too...

Hansen, Kooperberg, and Sardy (1998) introduced a family of continuous, piecewise linear functions defined over adaptively selected triangulations of the plane as a general approach to statistical modeling of bivariate densities, regression and hazard functions. These triograms enjoy a natural affine equivariance that offers distinct advantages over competing tensor product methods that are mor...

This paper proposes a novel approach that achieves shape registration by optimizing shape representation and transformation simultaneously, which are modeled by a constrained Gaussian Mixture Model (GMM) and a regularized thin plate spline respectively. The problem is formulated within a Bayesian framework and solved by an expectation-maximum (EM) algorithm. Compared with the popular methods ba...

Several nonparametric methods in a regression model are presented. First, the most classical ones: piecewise polynomial estimators, estimation with Spline bases, kernel estimators and projection estimators on orthonormal bases (such as Fourier or wavelet bases). Since these methods suffer from the curse of dimensionality, we also present Generalized Additive Models and CART regression models. T...

A linear mixed model with a smooth random effects density is proposed. A similar approach to P-spline smoothing of Eilers and Marx (1996, Statistical Science 11, 89-121) is applied to yield a more flexible estimate of the random effects density. Our approach differs from theirs in that the B-spline basis functions are replaced by approximating Gaussian densities. Fitting the model involves maxi...