Discriminant coordinates analysis for multivariate functional data

Abstract

One of the basic statistical methods of dimensionality reduction is analysis of discriminant coordinates given by Fisher (1936 Fisher, R. A. 1936. The use of multiple measurements in taxonomic problem. Annals of Eugenics 7 (2):179–88. doi:10.1111/j.1469-1809.1936.tb02137.x.[Crossref] , [Google Scholar]) and Rao (1948). The space of discriminant coordinates is a space convenient for presenting multidimensional data originating from multiple groups and for the use of various classification methods (methods of discriminant analysis). In the present paper, we adapt the classical discriminant coordinates analysis to multivariate functional data. The theory has been applied to analysis of textural properties of apples of six varieties, measured over a period of 180?days, stored in two types of refrigeration chamber.     

Bootstrap methods for multivariate hypothesis testing

The nonparametric and parametric bootstrap methods for multivariate hypothesis testing are developed. They are used to approximate the null distribution of the test statistics proposed by Duchesne and Francq (2015 Duchesne, P., Francq, C. (2015). Multivariate hypothesis testing using generalized and {2}-inverses—with applications. Statistics 49:475–496.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]), resulting in bootstrap testing procedures. In the problem of testing for the mean vector of a multivariate distribution, the asymptotic validity of the bootstrap methods is proved. The finite sample performance of the new solutions is demonstrated by means of Monte Carlo simulation studies. They indicate that for small-sample size, the bootstrap tests provide a better finite sample properties than the asymptotic tests considered by Duchesne and Francq (2015 Duchesne, P., Francq, C. (2015). Multivariate hypothesis testing using generalized and {2}-inverses—with applications. Statistics 49:475–496.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]).     

Sleeping with a Vampire

The research explores the narrative frames of recent vampire TV and cinematic stories with a special focus on the construction of gender relations and female agency in order to determine whether these narratives conjure up any liberating or empowering potential for their audience. The most significant elements of vampire fiction are taken into account: the central position of the heroine, power relations, love triangles, and the objectification of male characters. The identified common patterns both reflect and generate a crucial shift in Western sexual politics; at the same time, they address the burning concerns this shift has aroused, invoking the anthropological concept of “reflexivity.” The analysis is further couched in the debate on female empowerment and post-feminist gender representations in popular culture, including the idea of sexual subjectivity and a diagnosis of post-feminism as “double entanglement.” The described changes within the contemporary vampire narratives should not be, however, simplified as new forms of sexist discourse.     

Two Means Together? Effects of Response Bias and Sensitivity on Communicative Action Detection

Numerous lines of research suggest that communicative dyadic actions elicit preferential processing and more accurate detection compared to similar but individual actions. However, it is unclear whether the presence of the second agent provides additional cues that allow for more accurate discriminability between communicative and individual intentions or whether it lowers the threshold for perceiving third-party encounters as interactive. We performed a series of studies comparing the recognition of communicative actions from single and dyadic displays in healthy individuals. A decreased response threshold for communicative actions was observed for dyadic vs. single-agent animations across all three studies, providing evidence for the dyadic communicative bias. Furthermore, consistent with the facilitated recognition hypothesis, congruent response to a communicative gesture increased the ability to accurately interpret the actions. In line with dual-process theory, we propose that both mechanisms may be perceived as complementary rather than competitive and affect different stages of stimuli processing.

     

First and Second Derivatives in Time Series Classification Using DTW

In our previous work, we developed a new distance function based on a derivative and showed that our algorithm is effective. In contrast to well-known measures from the literature, our approach considers the general shape of a time series rather than standard distance of function (value) comparison. The new distance was used in classification with the nearest neighbor rule. Now we improve on our previous technique by adding the second derivative. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 47 time series datasets from a wide variety of application domains. Our experiments show that this new method provides a significantly more accurate classification on the examined datasets.     

Winter habitat selection of Corvids in an urban ecosystem

Urban Ecosystems - Urban areas attract birds during the winter when cities provide a predictable source of food and relatively stable weather conditions. However, many other factors determine the...     

A note on variable selection in functional regression via random subspace method

Variable selection problem is one of the most important tasks in regression analysis, especially in a high-dimensional setting. In this paper, we study this problem in the context of scalar response functional regression model, which is a linear model with scalar response and functional regressors. The functional model can be represented by certain multiple linear regression model via basis expansions of functional variables. Based on this model and random subspace method of Mielniczuk and Teisseyre (Comput Stat Data Anal 71:725–742, 2014), two simple variable selection procedures for scalar response functional regression model are proposed. The final functional model is selected by using generalized information criteria. Monte Carlo simulation studies conducted and a real data example show very satisfactory performance of new variable selection methods under finite samples. Moreover, they suggest that considered procedures outperform solutions found in the literature in terms of correctly selected model, false discovery rate control and prediction error.