Claudia Becker

Functional cerebral asymmetries during the menstrual cycle: a cross-sectional and longitudinal analysis

This study aims at answering two basic questions regarding the mechanisms with which hormones modulate functional cerebral asymmetries. Which steroids or gonadotropins fluctuating during the menstrual cycle affect perceptual asymmetries? Can these effects be demonstrated in a cross-sectional (follicular and midluteal cycle phases analyzed) and a longitudinal design, in which the continuous hormone and asymmetry fluctuations were measured over a time course of 6 weeks? To answer these questions, 12 spontaneously cycling right-handed women participated in an experiment in which their levels of progesterone, estradiol, testosterone, LH, and FSH were assessed every 3 days by blood-sample based radioimmunoassays (RIAs). At the same points in time their asymmetries were analyzed with visual half-field (VHF) techniques using a lexical decision, a figure recognition, and a face discrimination task. Both cross-sectional and longitudinal analyzes showed that an increase of progesterone is related to a reduction in asymmetries in a figure recognition task by increasing the performance of the left-hemisphere which is less specialized for this task. Cross-sectionally, estradiol was shown to have significant relationships to the accuracy and the response speed of both hemispheres. However, since these effects were in the same direction, asymmetry was not affected. This was not the case in the longitudinal design, where estradiol affected the asymmetry in the lexical decision and the figural comparison task. Overall, these data show that hormonal fluctuations within the menstrual cycle have important impacts on functional cerebral asymmetries. The effect of progesterone was highly reliable and could be shown in both analysis schemes. By contrast, estradiol mainly, but not exclusively, affected both hemispheres in the same direction.

The Masking Breakdown Point of Multivariate Outlier Identification Rules

Abstract In this article, we consider simultaneous outlier identification rules for multivariate data, generalizing the concept of so-called α outlier identifiers, as presented by Davies and Gather for the case of univariate samples. Such multivariate outlier identifiers are based on estimators of location and covariance. Therefore, it seems reasonable that characteristics of the estimators influence the behavior of outlier identifiers. Several authors mentioned that using estimators with low finite-sample breakdown point is not recommended for identifying outliers. To give a formal explanation, we investigate how the finite-sample breakdown points of estimators used in these identification rules influence the masking behavior of the rules.

The largest nonindentifiable outlier: a comparison of multivariate simultaneous outlier identification rules

The aim of detecting outliers in a multivariate sample can be pursued in different ways. We investigate here the performance of several simultaneous multivariate outlier identification rules based on robust estimators of location and scale. It has been shown that the use of estimators with high finite sample breakdown point in such procedures yields a good behaviour with respect to the prevention of breakdown by the masking effect (Becker, Gather 1999, J. Amer. Statist. Assoc. 94, 947-955). In this article, we investigate by simulation, at which distance from the center of an underlying model distribution outliers can be placed until certain simultaneous identification rules will detect them as outliers. We consider identification procedures based on the minimum volume ellipsoid, the minimum covariance determinant, and S-estimators.

Perfectionism, achievement motives, and attribution of success and failure in female soccer players

While some researchers have identified adaptive perfectionism as a key characteristic to achieving elite performance in sport, others see perfectionism as a maladaptive characteristic that undermines, rather than helps, athletic performance. Arguing that perfectionism in sport contains both adaptive and maladaptive facets, the present article presents a study of N = 74 female soccer players investigating how two facets of perfectionism-perfectionistic strivings and negative reactions to imperfection (Stoeber, Otto, Pescheck, Becker, & Stoll, 2007 )-are related to achievement motives and attributions of success and failure. Results show that striving for perfection was related to hope of success and self-serving attributions (internal attribution of success). Moreover, once overlap between the two facets of perfectionism was controlled for, striving for perfection was inversely related to fear of failure and self-depreciating attributions (internal attribution of failure). In contrast, negative reactions to imperfection were positively related to fear of failure and self-depreciating attributions (external attribution of success) and inversely related to self-serving attributions (internal attribution of success and external attribution of failure). It is concluded that striving for perfection in sport is associated with an adaptive pattern of positive motivational orientations and self-serving attributions of success and failure, which may help athletic performance. In contrast, negative reactions to imperfection are associated with a maladaptive pattern of negative motivational orientations and self-depreciating attributions, which is likely to undermine athletic performance. Consequently, perfectionism in sport may be adaptive in those athletes who strive for perfection, but can control their negative reactions when performance is less than perfect.

A note On outlier sensitivity of Sliced Inverse Regression

Sliced Inverse Regression (SIR) is a promising technique for the purpose of dimension reduction. Several properties of this method have been examined already, but little attention has been paid to robustness aspects. In this article, we focus on the sensitivity of SIR to outliers and show in what sense and how severely SIR can be influenced by outliers in the data.

A Robustified Version of Sliced Inverse Regression

Sliced Inverse Regression (SIR) (Li, 1991) is a method for dimension reduction in (nonparametric) regression models, based on the idea of using the information contained in the inverse regression curve. In the various steps of the SIR procedure, classical statistical estimators are used. Thus, the resulting method is nonrobust, and an immediate possibility to robustify SIR is to replace the classical estimators by robust ones. This leads to procedures which maintain the clever estimation scheme of the original SIR method but can cope better with outliers in the regressor space. We present such robustified versions of SIR and compare them with the original procedure and among each other under different model assumptions.

Sensitivity of graphical modeling against contamination

Graphical modeling as a form of multivariate analysis has turned out to be a capable tool for the detection and modeling of complex dependency structures. Statistical models are related to graphs, in which variables are represented by points and associations between each two of them as lines. The usefulness of graphical modeling depends of course on finding a graphical model, which fits the data appropriately. We will investigate how existing model building strategies and estimation methods can be affected by model disturbances or outlying observations. The focus of our sensitivity analysis lies on mixed graphical models, where both discrete and continuous variables are considered.

Sliced Inverse Regression for High-dimensional Time Series

Methods of dimension reduction are very helpful and almost a necessity when analyzing high-dimensional time series since otherwise modelling affords many parameters because of interactions at various time-lags. We use a dynamic version of Sliced InverseRegression (SIR; (1991)) as an exploratory tool for analyzing multivariate time series. Analyzing each variable individually, wesearch for those directions, i.e, linear combinations of past and present observations of the other variables which explain most of its variability. This also provides information on possible nonlinearities. An application to time series representing the hemodynamic system is given.

The flood algorithm--a multivariate, self-organizing-map-based, robust location and covariance estimator

Self-organizing maps (SOMs) introduced by Kohonen (Biol. Cybern. 43(1):59–69, 1982) are well-known in the field of artificial neural networks. The way SOMs are performing is very intuitive, leading to great popularity and numerous applications (related to statistics: classification, clustering). The result of the unsupervised learning process performed by SOMs is a non-linear, low-dimensional projection of the high-dimensional input data, that preserves certain features of the underlying data, e.g. the topology and probability distribution (Lee and Verleysen in Nonlinear Dimensionality Reduction, Springer, 2007; Kohonen in Self-organizing Maps, 3rd edn., Springer, 2001).With the U-matrix Ultsch (Information and Classification: Concepts, Methods and Applications, pp. 307–313, Springer, 1993) introduced a powerful visual representation of the SOM results. We propose an approach that utilizes the U-matrix to identify outlying data points. Then the revised subsample (i.e. the initial sample minus the outlying points) is used to give a robust estimation of location and scatter.

Iterative Proportional Scaling Based on a Robust Start Estimator

Model selection procedures in graphical modeling are essentially based on the estimation of covariance matrices under conditional independence restrictions. Such model selection procedures can react heavily on the presence of outlying observations. One reason for this might be that the covariance estimation is influenced by outliers. Hence, a robust procedure to estimate a covariance matrix under conditional independence restrictions is needed. As a first step to robustify the model building process in graphical modeling we propose to use a modified iterative proportional scaling algorithm, starting with a robust covariance estimator.