The L1 norm has been applied in numerous variations of principal component analysis (PCA). An L1-norm PCA is an attractive alternative to traditional L2-based PCA because it can impart robustness in the presence of outliers and is indicated for models where standard Gaussian assumptions about...

Four algorithms for sequencing runs of the 2n factorial experiment are compared according to the two criteria: the number of factor level changes (i.e. cost) and the time trend resistance. These algorithms are: Correa et al. (2009), Cui and John (1998), Cheng and Jacroux (1988) and Coster and...

The graphical lasso (glasso) is a widely-used fast algorithm for estimating sparse inverse covariance matrices. The glasso solves an ℓ1 penalized maximum likelihood problem and is available as an R library on CRAN. The output from the glasso, a regularized covariance matrix estimate Σˆglasso...

Classification problems involving a categorical class label Y and a functional predictor X(t) are becoming increasingly common. Since X(t) is infinite dimensional, some form of dimension reduction is essential in these problems. Conventional dimension reduction techniques for functional data...

We apply the generalized cluster bootstrap to both Gaussian quasi-likelihood and robust estimates in the context of highly unbalanced clustered data. We compare it with the transformation bootstrap where the data are generated by the random effect and transformation models and all the random...

Pareto-type distributions are extreme value distributions for which the extreme value index γ0. Classical estimators for γ0, like the Hill estimator, tend to overestimate this parameter in the presence of outliers. The empirical influence function plot, which displays the influence that each...

Methods for the parameter estimation for a spatio-temporal marked point process model, the so-called growth-interaction model, are investigated. Least squares estimation methods for this model found in the literature are only concerned with fitting the mark distribution observed in the data....

We propose a modified nonparametric Baumgartner–Weiß–Schindler test and investigate its use in testing for trends among K binomial populations. Exact conditional and unconditional approaches to p-value calculation are explored in conjunction with the statistic in addition to a similar test...

In clinical studies, covariates are often measured with error due to biological fluctuations, device error and other sources. Summary statistics and regression models that are based on mis-measured data will differ from the corresponding analysis based on the “true” covariate. Statistical...

Robust distances are mainly used for the purpose of detecting multivariate outliers. The precise definition of cut-off values for formal outlier testing assumes that the “good” part of the data comes from a multivariate normal population. Robust distances also provide valuable information on...