Assessing the pattern of covariance matrices via an augmentation multiple testing procedure

This paper extends the scedasticity comparison among several groups of observations, usually complying with the homoscedastic and the heteroscedastic cases, in order to deal with data sets laying in an intermediate situation. As is well known, homoscedasticity corresponds to equality in orientation, shape and size of the group scatters. Here our attention is focused on two weaker requirements: scatters with the same orientation, but with different shape and size, or scatters with the same shape and size but different orientation. We introduce a multiple testing procedure that takes into account each of the above conditions. This approach discloses a richer information on the data underlying structure than the classical method only based on homo/heteroscedasticity. At the same time, it allows a more parsimonious parametrization, whenever the patterned model is appropriate to describe the real data. The new inferential methodology is then applied to some well-known data sets, chosen in the multivariate literature, to show the real gain in using this more informative approach. Finally, a wide simulation study illustrates and compares the performance of the proposal using data sets with gradual departure from homoscedasticity.     

Family Structure and Subjective Economic Well-Being: Some New Evidence

The paper investigates the main socio-demographic and economic determinants of subjective economic well-being in different typologies of households. Previous studies have used dummy variables to explore the effect of family structure. In this paper, however four different models—one for each family typology—have been estimated to test if each selected explanatory variable is significant and how it acts in determining the level of subjective economic well-being. To achieve this, we apply an under-used logit model—the partial proportional ordered model. Our analysis, based on data from the 2005 Italian Survey on Income and Living Conditions highlights the main variables affecting the subjective economic well-being of all household typologies. These main variables are related to income adequacy, such as being able to afford housing, clothes and holidays, and also include the work-status and level of education of the respondent. These variables produce notable differences in the level of subjective economic well-being. We find that couples with no children have the highest level of perceived economic well-being, while couples with two or more children and even more so one-person households are more economically insecure.     

Calibration of FI‐ISK,an Invasiveness Screening Tool for Nonnative Freshwater Invertebrates

The Freshwater Invertebrate Invasiveness Scoring Kit (FI‐ISK) is proposed as a screening tool for identifying potentially invasive freshwater invertebrates. FI‐ISK was adapted from the Fish Invasiveness Scoring Kit (FISK) of Copp, Garthwaite, and Gozlan, which is an adapted form of the Weed Risk Assessment (WRA) of Pheloung, Williams, and Halloy. Initial assessments using FI‐ISK, which include confidence (certainty/uncertainty) rankings by the assessor to each response, were calibrated to determine the most appropriate score thresholds for classifying nonnative species into low‐, medium‐, and high‐risk categories, using both the original medium‐to‐high risk threshold scores for the WRA (i.e., ≥6) and for FISK (i.e., ≥19). Patterns of the assessor's confidence, when making the responses during the FI‐ISK assessments, were also examined. Using receiver operating characteristic (ROC) curves, FI‐ISK was shown to distinguish accurately (and with statistical confidence) between potentially invasive and noninvasive species of nonnative crayfish (Decapoda: Astacidae, Cambaridae, Parastacidae), with the statistically appropriate threshold score for high‐risk species scores being ≥16. FI‐ISK represents a useful and viable tool to aid decision‐ and policymakers in assessing and classifying freshwater invertebrates according to their potential invasiveness.     

Newborns' Perceptual Categorization for Closed and Open Geometric Forms

This study explored newborns' ability to perceive perceptual similarities between different exemplars of 2 broad classes of simple shapes: closed and open geometric forms. Three experiments were carried out using a visual paired‐comparison task. Evidence showed that, after familiarization either to closed‐shaped or to open‐shaped forms, newborns manifested a novelty preference for a novel‐category rather than for a familiar‐category exemplar (Experiment 1). This result could not be explained either as a consequence of the newborns' inability to discriminate between instances of the same category of simple geometric forms (Experiment 2), or as a consequence of a spontaneous preference for the novel‐category exemplars (Experiment 3). Overall, findings revealed that newborns are able to form broad categories of distinguishable geometric shapes by relying on the shapes' perceptual similarity.     

Parameter estimation in a verhulst stochastic model

Summary Maximum likelihood estimation is a well-known statistical tool. When applied to the study of dynamical continuous-time phenomena, it requires some specific assumptions. In this paper we discuss some properties of the maximum likelihood estimator for a stochastic Verhulst model and we show some simulation results on the behaviour of the corresponding discrete estimator.     

Depth Measures for Multivariate Functional Data

In this article, we address the problem of mining and analyzing multivariate functional data. That is, data where each observation is a set of possibly correlated functions. Complex data of this kind is more and more common in many research fields, particularly in the biomedical context. In this work, we propose and apply a new concept of depth measure for multivariate functional data. With this new depth measure it is possible to generalize robust statistics, such as the median, to the multivariate functional framework, which in turn allows the application of outlier detection, boxplots construction, and nonparametric tests also in this more general framework. We present an application to Electrocardiographic (ECG) signals.     

Functional principal component analysis for the explorative analysis of multisite–multivariate air pollution time series with long gaps

The knowledge of the urban air quality represents the first step to face air pollution issues. For the last decades many cities can rely on a network of monitoring stations recording concentration values for the main pollutants. This paper focuses on functional principal component analysis (FPCA) to investigate multiple pollutant datasets measured over time at multiple sites within a given urban area. Our purpose is to extend what has been proposed in the literature to data that are multisite and multivariate at the same time. The approach results to be effective to highlight some relevant statistical features of the time series, giving the opportunity to identify significant pollutants and to know the evolution of their variability along time. The paper also deals with missing value issue. As it is known, very long gap sequences can often occur in air quality datasets, due to long time failures not easily solvable or to data coming from a mobile monitoring station. In the considered dataset, large and continuous gaps are imputed by empirical orthogonal function procedure, after denoising raw data by functional data analysis and before performing FPCA, in order to further improve the reconstruction.     

Digital learning across cultures: an account of activity theory

ABSTRACT

This paper explores a teaching and learning process as it unfolds in an online offshore classroom. The paper reports on teaching and learning narratives distilled from different cultures, namely the cultures of educators and students in the context of digital learning environments. Drawing on activity theory as a conceptual framework to analyse the narrative, we employ self-study as a methodological tool for capturing the dynamism and complexities that unravel in intercultural and technology-enabled teaching activities. The framework explores the relationships, tensions and opportunities of diverse activity elements that constitute the design and delivery of digital pedagogies. In the networked intercultural system, sustaining the position of activity elements and the specific relational circuit that they institutionalise is a key task for understanding digital technologies as an influential tool for effective learning and teaching practices. We argue that seeing the digital platform from the perspective of its role in creating dynamic interrelationships in a complex activity system is one way to move beyond the cultural confines of any particular element in the system.