Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

Authors' reply to the discussion of 'Automatic change-point detection in time series via deep learning' at the discussion meeting on 'Probabilistic and statistical aspects of machine learning'

We would like to thank the proposer, seconder, and all discussants for their time in reading our article and their thought-provoking comments. We are glad to find a broad consensus that neural-network-based approach offers a flexible framework for automatic change-point analysis. There are a number of common themes to the comments, and we have therefore structured our response around the topics of the theory, training, the importance of standardization and possible extensions, before addressing some of the remaining individual comments

Proposer of the vote of thanks and contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’

Computational statistics and machine learning (ML) are closely related, and there are many opportunities for cross-fertilization of ideas between the two fields. Both can benefit from greater interaction, and the two papers being discussed here highlight some ways that this can happen

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

New Directions for Contact Integrators

Contact integrators are a family of geometric numerical schemes which guarantee the conservation of the contact structure. In this work we review the construction of both the variational and Hamiltonian versions of these methods. We illustrate some of the advantages of geometric integration in the dissipative setting by focusing on models inspired by recent studies in celestial mechanics and cosmology.Comment: To appear as Chapter 24 in GSI 2021, Springer LNCS 1282

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Proceedings of the 35th International Workshop on Statistical Modelling : July 20- 24, 2020 Bilbao, Basque Country, Spain

466 p.The InternationalWorkshop on Statistical Modelling (IWSM) is a reference workshop in promoting statistical modelling, applications of Statistics for researchers, academics and industrialist in a broad sense. Unfortunately, the global COVID-19 pandemic has not allowed holding the 35th edition of the IWSM in Bilbao in July 2020. Despite the situation and following the spirit of the Workshop and the Statistical Modelling Society, we are delighted to bring you the proceedings book of extended abstracts

Proceedings of the 35th International Workshop on Statistical Modelling : July 20- 24, 2020 Bilbao, Basque Country, Spain

466 p.The InternationalWorkshop on Statistical Modelling (IWSM) is a reference workshop in promoting statistical modelling, applications of Statistics for researchers, academics and industrialist in a broad sense. Unfortunately, the global COVID-19 pandemic has not allowed holding the 35th edition of the IWSM in Bilbao in July 2020. Despite the situation and following the spirit of the Workshop and the Statistical Modelling Society, we are delighted to bring you the proceedings book of extended abstracts

Species Composition and Spatial Ecology of Amazonian Understory Mixed-Species Flocks in a Fragmented Landscape

With the ongoing advance of the agricultural frontier in the Amazon basin, it is inevitable that heterogeneous landscapes will play a key role in conservation. These landscapes are mostly composed of patchworks of small forest fragments, secondary forests and roads. Conservation, however must take species interactions into consideration as they play a pivotal part the maintenance of several biological processes in the tropics. One of the most conspicuous interspecific interactions are seen in mixed-species flocks of birds, which in the Amazon, represent one of the best organized systems of bird aggregations. In this research, I assess how flock spatial behavior and species compositions are affected by changes in habitat structure. I followed 29 mixed-species flocks in different landscapes types such as secondary forests, forest fragments of 10 and 100 ha, and mixes of primary and secondary forest patches. As flocks foraged through their territories, I recorded their species composition every 30 minutes and georeferenced their movements every 30 seconds. Flocks spatial behavior was severely affected by anthropogenic features such as forest edges and secondary forests as flocks respond strongly to vegetation height. Using step-selection models, it was possible to reproduce flock movements and show that they prefer taller vegetation and lower areas of topography such as stream valleys. Due to this behavior, flocks avoided areas where canopy height was below 15 meters, and extensive areas of secondary below this height hold unstable flocks that do not persist for long periods. The ones that persisted showed home ranges that were much larger than what was observed in primary forest. Time spent in secondary forest was dependent on vegetation height, but not area, which seems to be shaped by intraspecific interactions. Flock social structure is also severely affected by habitat structure. Flock species richness did not show a predictable pattern, but participation was negatively affected. In fact, our data indicates that flock social structure may take longer to recover than spatial behavior. Assessing a 30-year mist-net capture dataset, we were able to determine that indeed, decreased species participation seems to be a more important driver in flock dissolution than local extinction