Automatic bandwidth selection for circular density estimation

Given angular data θ1,…,θn[0,2π) a common objective is to estimate the density. In case that a kernel estimator is used, bandwidth selection is crucial to the performance. A “plug-in rule” for the bandwidth, which is based on the concentration of a reference density, namely, the von Mises distribution is obtained. It is seen that this is equivalent to the usual Euclidean plug-in rule in the case where the concentration becomes large. In case that the concentration parameter is unknown, alternative methods are explored which are intended to be robust to departures from the reference density. Simulations indicate that “wrapped estimators” can perform well in this context. The methods are applied to a real bivariate dataset concerning protein structure

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

A mixture transition distribution modeling for higher-order circular Markov processes

The stationary higher-order Markov process for circular data is considered. We employ the mixture transition distribution (MTD) model to express the transition density of the process on the circle. The underlying circular transition distribution is based on Wehrly and Johnson's bivariate joint circular models. The structures of the circular autocorrelation function together with the circular partial autocorrelation function are found to be similar to those of the autocorrelation and partial autocorrelation functions of the real-valued autoregressive process when the underlying binding density has zero sine moments. The validity of the model is assessed by applying it to some Monte Carlo simulations and real directional data

Winter Torpor and Roosting Ecology of Tri-Colored Bats (\u3ci\u3ePerimyotis subflavus\u3c/i\u3e) in Trees and Bridges

Subterranean hibernating tri-colored bats (Perimyotis subflavus) have experienced precipitous declines from white-nose syndrome (WNS). However, tri-colored bats also use thermally unstable roosts like tree cavities, bridges, and foliage during winter. Our objective was to determine where tri-colored bats (Perimyotis subflavus) using thermally unstable roosts lie on the torpor continuum to understand their potential WNS susceptibility, as well as determine roost use and selection in an area devoid of subterranean roosts. From November to March 2017-2019, we used temperature-sensitive radio-transmitters to track bats to their day roosts and document their torpor and activity patterns on the Savannah River Site in south-central South Carolina. We measured habitat and tree characteristics of 24 used trees and 153 random trees and used discrete choice models to determine selection. Torpid bout duration (mean 2.7 ± 2.8 days SD) was negatively related to ambient temperature and positively related to precipitation. Bats maintained a non-random arousal pattern focused near dusk and were active on 33.6% of tracked days. Of arousals, 51% contained a passive rewarming component. Normothermic bout duration, general activity, and activity away from the roost were positively related to ambient temperature, and activity away from the roost was negatively related to barometric pressure. Days were cooler (8.7°C ± 5.0) when bats used bridges than on days that they used trees (11.3°C ± 5.4). Roost selection was negatively related to stream distance and tree decay state and positively related to canopy closure and cavity abundance. Bats also appeared to favor hardwood forests and avoid pine forests. Tri-colored bats using thermally unstable roosts at SRS displayed winter torpor more reminiscent of daily torpor than classic hibernation. Our results suggest tri-colored bats in thermally unstable roosts may be less susceptible to white-nose syndrome than hibernating tri-colored bats in thermally stable roosts. Our results also suggest that access to multiple roost microclimates may be important for tri-colored bats during winter and forest management practices which retain live trees near streams with multiple roosting structures and foster cavity formation in hardwood forests will likely benefit this population. An understanding of tri-colored bat winter torpor and roosting ecology in areas devoid of subterranean roosts is increasingly important due to WNS-related declines of populations using subterranean hibernacula

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