Editorial note

The paper addresses the recent flurry of beauty pageants as reintegration rituals which specifically aim at the symbolic integration of some stigmatized embodied identities: Miss HIV (Botswana, Uganda, Nigeria, Zimbabwe but also Russia), Mr or Ms AIDS (Kenya) and the most recent Miss Landmine (Angola, Cambodia). Common reactions to such events betray a most uncomfortable moral quandary: people seem torn between condemnation, repulsion and a very hesitant acknowledgement of the stated aim of positive re-integration. The paper explores this moral discomfort through its relations to a number of unresolved issues: the ambiguous status of beauty, the complex relationships between stigma and (its lack of) public representation, the multiple uses of beauty pageants as integrative rituals and the importance of beauty practices as a means to re-create meaning and dignity in distressing circumstances. Contestants’ interviews make it clear that they use the beauty pageants as one of the few – or maybe the only – site allowing for personal, social and political affirmation. The necessary collective dimension of these affirmations is linked to the socio-cultural and political contexts of countries just re-emerging from armed struggle.     

Weighted Wilcoxon Estimators in Nonlinear Regression

In this paper, we consider the estimation of parameters of a general near regression model. An estimator that minimises the weighted Wilcoxon dispersion function is considered and its asymptotic properties established under mild regularity conditions similar to those used in least squares and least absolute deviations estimation. As in linear models, the procedure provides estimators that are robust and highly efficient. The estimates depend on the choice of a weight function and diagnostics which differentiate between nonlinear fits are provided along with appropriate benchmarks. The behavior of these estimates is discussed on a real data set. A simulation study verifies the robustness, efficiency and validity of these estimates over several error distributions including the normal and a family of contaminated normal distributions.     

Images: The Seesaw Haunting keeps Killing the Living

When history speaks through bloodshed for too long, through unashamed violence and, ideological abuse, when submission is the line of last resistance, when man has no respect for man, the individual, the different, when authority appropriates all powers, even that of life and death, when (brute) force supplants the rational and the reasonable, then the solitary protester, the impotent bystander has no choice but to rise up and, despite it all, assert his dignity regeneration of identity. (Alvim, 1995)     

Rank-based group variable selection

A robust rank-based estimator for variable selection in linear models, with grouped predictors, is studied. The proposed estimation procedure extends the existing rank-based variable selection [Johnson, B.A., and Peng, L. (2008), ‘Rank-based Variable Selection’, Journal of Nonparametric Statistics, 20(3):241–252] and the ww-scad [Wang, L., and Li, R. (2009), ‘Weighted Wilcoxon-type Smoothly Clipped Absolute Deviation Method’, Biometrics, 65(2):564–571] to linear regression models with grouped variables. The resulting estimator is robust to contamination or deviations in both the response and the design space.The Oracle property and asymptotic normality of the estimator are established under some regularity conditions. Simulation studies reveal that the proposed method performs better than the existing rank-based methods [Johnson, B.A., and Peng, L. (2008), ‘Rank-based Variable Selection’, Journal of Nonparametric Statistics, 20(3):241–252; Wang, L., and Li, R. (2009), ‘Weighted Wilcoxon-type Smoothly Clipped Absolute Deviation Method’, Biometrics, 65(2):564–571] for grouped variables models. This estimation procedure also outperforms the adaptive hlasso [Zhou, N., and Zhu, J. (2010), ‘Group Variable Selection Via a Hierarchical Lasso and its Oracle Property’, Interface, 3(4):557–574] in the presence of local contamination in the design space or for heavy-tailed error distribution.     

Smooth Nonparametric Allocation of Classification

A nonparametric discriminant analysis procedure that is robust to deviations from the usual assumptions is proposed. The procedure uses the projection pursuit methodology where the projection index is the two-group transvariation probability. We use allocation based on the centrality of the new point measured using a smooth version of point-group transvariation. It is shown that the new procedure provides lower misclassification error rates than competing methods for data from skewed heavy-tailed and skewed distributions as well as unequal training data sizes.     

Robust confidence regions for the semi-parametric regression model with responses missing at random

In this paper, a regression semi-parametric model is considered where responses are assumed to be missing at random. From the empirical likelihood function defined based on the rank-based estimating equation, robust confidence intervals/regions of the true regression coefficient are derived. Monte Carlo simulation experiments show that the proposed approach provides more accurate confidence intervals/regions compared to its normal approximation counterpart under different model error structure. The approach is also compared with the least squares approach, and its superiority is shown whenever the error distribution in the simulation study is heavy tailed or contaminated. Finally, a real data example is given to illustrate our proposed method.