Proposal of the Dichotomous STATIS DUAL Method: Software and Application for the Analysis of Dichotomous Data, Applied to the Test of Learning Styles in University Students

The present work analyzed a review of methods for analyzing sequences of matrices or dichotomous data. A new method for a sequence of dichotomous matrices with a different number of rows is presented; the Dichotomous STATIS DUAL. Suppose we match the sequence of matrices by different years, with this method. In that case, we can graphically represent the relations among the different columns of all the matrices, and the relations between those columns and the different years, because everything can be represented in the same plots. As in all STATIS methods, three different plots can get (i) the interstructure, with the relations among the years; (ii) the compromise, with the stable part of the relations between the columns; and (iii) the intrastructure (also known as trajectories), with the relations between columns and years, in other words, the evolution of the columns through the time. This new mathematical method can be used with all kinds of dichotomous data, thanks to the software we propose. In the present work, the software was applied to the assessment of learning styles

Randomization tests for peer effects in group formation experiments

Measuring the effect of peers on individual outcomes is a challenging problem, in part because individuals often select peers who are similar in both observable and unobservable ways. Group formation experiments avoid this problem by randomly assigning individuals to groups and observing their responses; for example, do first-year students have better grades when they are randomly assigned roommates who have stronger academic backgrounds? Standard approaches for analyzing these experiments, however, are heavily model-dependent and generally fail to exploit the randomized design. In this paper, we extend methods from randomization-based testing under interference to group formation experiments. The proposed tests are justified by the randomization itself, require relatively few assumptions, and are exact in finite samples. First, we develop procedures that yield valid tests for arbitrary group formation designs. Second, we derive sufficient conditions on the design such that the randomization test can be implemented via simple random permutations. We apply this approach to two recent group formation experiments

Study of Temporal Variations in Species–Environment Association through an Innovative Multivariate Method: MixSTATICO

[EN]The study of biotic and abiotic factors and their interrelationships is essential in the preservation of sustainable marine ecosystems and for understanding the impact that climate change can have on different species. For instance, phytoplankton are extremely vulnerable to environmental changes and thus studying the factors involved is important for the species’ conservation. This work examines the relationship between phytoplankton and environmental parameters of the eastern equatorial Pacific, known as one of the most biologically rich regions in the world. For this purpose, a new multivariate method called MixSTATICO has been developed, allowing mixed-type data structured in two different groups (environment and species) to be related and measured on a space–time scale. The results obtained show how seasons have an impact on species–environment relations, with the most significant association occurring in November and the weakest during the month of May (change of season). The species Lauderia borealis, Chaetoceros didymus and Gyrodinium sp. were not observed in the coastal profiles during the dry season at most stations, while during the rainy season, the species Dactyliosolen antarcticus, Proboscia alata and Skeletonema costatum were not detected. Using MixSTATICO, species vulnerable to specific geographical locations and environmental variations were identified, making it possible to establish biological indicators for this region

A survey of distributed data aggregation algorithms

Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, which can then be used to direct the execution of other applications. The resulting values are derived by the distributed computation of functions like COUNT, SUM, and AVERAGE. Some application examples deal with the determination of the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.info:eu-repo/semantics/publishedVersio

Contribuciones a los métodos STASIS basados en técnicas de aprendizaje no supervisado

[ES] 1. La revisión bibliográfica ha mostrado que los métodos STATIS se han desarrollado ampliamente, sobre todo a partir del año 2000. Desde la propuesta original de L’Hermier des Plantes en 1976 con STATIS y STATIS-dual(L’Hermier des Plantes, 1976), y la de Jaffrenou en 1978 con X-STATIS o PTA, no se habían presentado nuevos métodos de esta familia. A partir del año 2004 se presentan 17 nuevas propuestas, de las cuales las más recientes son: Sir-STATIS, HiDiSTATIS , DiDiSTATIS, CATATIS y CLUSTATIS. 2. De la exhaustiva revisión bibliográfica realizada, hemos encontrado que los métodos sparse, se han aplicado a técnicas de análisis de datos de tres vías como lo son el Candecomp/Parafac y más recientemente a los métodos Tucker. 3. No se ha encontrado en la literatura referencias que implementen técnicas de restricción en los métodos STATIS. Es así, que nuestro principal aporte es una nueva propuesta de STATIS (Sparse STATIS-dual), basado en las restricciones Elastic Net. Al combinar las normas 1 y 2 , en la penalización, tiende a dar menos cargas establecidas en cero que un ajuste en un valor de la norma 1, y una mayor contracción de los otros coeficientes. 4. Consideramos que el uso de las técnicas de regularización sparse en el STATIS, permite obtener soluciones eficientes a problemas de alta dimensionalidad de los datos. 5. Para dar soporte al nuevo método del Sparse STATIS-dual planteado, se ha implementado una librería en el lenguaje de programación R, facilitando su aplicación a cualquier conjunto de datos mediante funciones específicas. El software desarrollado en R, llamado SparseSTATISdual, permite aplicar la propuesta teórica de esta tesis doctoral. 6. Se han desarrollado dos librerías más para los métodos STATIS (clásico) y STATIS-dual. 7. Se han comparado los resultados obtenidos mediante el STATIS-dual y el Sparse STATIS-dual, aplicados a datos sobre indicadores ambientales a nivel mundial, compilados en el Environmental Performance Index de la Universidad de Yale. De esta manera, se ha demostrado que al pasar del STATIS-dual a los resultados del Sparse STATIS-dual se ha reducido el número de variables, lo que hace más obtener una mejor interpretación de las variables. 8. Esta contribución abre las puertas para el desarrollo de nuevas aplicaciones de las Sparse en otras técnicas de la familia STATIS