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Compositional Data Analysis
The authoritative contributions gathered in this volume reflect the state of the art in compositional data analysis (CoDa). The respective chapters cover all aspects of CoDa, ranging from mathematical theory, statistical methods and techniques to its broad range of applications in geochemistry, the life sciences and other disciplines. The selected and peer-reviewed papers were originally presented at the 6th International Workshop on Compositional Data Analysis, CoDaWork 2015, held in L’Escala (Girona), Spain. Compositional data is defined as vectors of positive components and constant sum, and, more generally, all those vectors representing parts of a whole which only carry relative infor...
Compositional Data Analysis in the Geosciences
Since Karl Pearson wrote his paper on spurious correlation in 1897, a lot has been said about the statistical analysis of compositional data, mainly by geologists such as Felix Chayes. The solution appeared in the 1980s, when John Aitchison proposed to use Iogratios. Since then, the approach has seen a great expansion, mainly building on the idea of the `natural geometry' of the sample space. Statistics is expected to give sense to our perception of the natural scale of the data, and this is made possible for compositional data using Iogratios. This publication will be a milestone in this process.
Glioblastoma: State of the Art and Future Perspectives
Glioblastoma is an aggressive incurable primary tumor of the central nervous system. Median overall survival is in the range of 1.5 years even in selected clinical trials populations. Many features contribute to this therapeutic challenge including high intratumoral and intertumoral heterogeneity, resistance to therapy, migration and invasion, immunosuppression. With the access of novel highthroughput technologies, significant progress has been made to understand molecular and immunological signatures underlying the pathology of glioblastoma. Clinical trial designs have shifted from investigating broad “one-for-all” treatment approaches to precision oncology designs. The collection of contributions in this book aim at providing researchers and clinicians an update on different aspects of glioblastoma, i.e. progress in basic, preclinical and clinical research.
ASMOD 2018
[English]:This volume collects the peer-reviewed contributions presented at the 2nd International Conference on “Advances in Statistical Modelling of Ordinal Data” - ASMOD 2018 - held at the Department of Political Sciences of the University of Naples Federico II (24-26 October 2018). The Conference brought together theoretical and applied statisticians to share the latest studies and developments in the field. In addition to the fundamental topic of latent structure analysis and modelling, the contributions in this volume cover a broad range of topics including measuring dissimilarity, clustering, robustness, CUB models, multivariate models, and permutation tests. The Conference feature...
ISI Directory
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Compositional Data Analysis
It is difficult to imagine that the statistical analysis of compositional data has been a major issue of concern for more than 100 years. It is even more difficult to realize that so many statisticians and users of statistics are unaware of the particular problems affecting compositional data, as well as their solutions. The issue of ``spurious correlation'', as the situation was phrased by Karl Pearson back in 1897, affects all data that measures parts of some whole, such as percentages, proportions, ppm and ppb. Such measurements are present in all fields of science, ranging from geology, biology, environmental sciences, forensic sciences, medicine and hydrology. This book presents the his...
Qüestiió
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Guía de departamentos universitarios 1989
Guía que se realiza para dar cumplimiento a la Ley 11/83 de Reforma Universitaria y Decretos que la desarrollan.
