The joint projected normal and skew-normal: a distribution for poly-cylindrical data

The contribution of this work is the introduction of a multivariate circular-linear (or poly- cylindrical) distribution obtained by combining the projected and the skew-normal. We show the flexibility of our proposal, its property of closure under marginalization and how to quantify multivariate dependence. Due to a non-identifiability issue that our proposal inherits from the projected normal, a compu- tational problem arises. We overcome it in a Bayesian framework, adding suitable latent variables and showing that posterior samples can be obtained with a post-processing of the estimation algo- rithm output. Under specific prior choices, this approach enables us to implement a Markov chain Monte Carlo algorithm relying only on Gibbs steps, where the updates of the parameters are done as if we were working with a multivariate normal likelihood. The proposed approach can be also used with the projected normal. As a proof of concept, on simulated examples we show the ability of our algorithm in recovering the parameters values and to solve the identification problem. Then the proposal is used in a real data example, where the turning-angles (circular variables) and the logarithm of the step-lengths (linear variables) of four zebras are jointly modelled

Modeling animal movement with directional persistence and attractive points

GPS technology is currently easily accessible to researchers, and many animal movement datasets are available. Two of the main features that a model which describes an animal's path can possess are directional persistence and attraction to a point in space. In this work, we propose a new approach that can have both characteristics. Our proposal is a hidden Markov model with a new emission distribution. The emission distribution models the two aforementioned characteristics, while the latent state of the hidden Markov model is needed to account for the behavioral modes. We show that the model is easy to implement in a Bayesian framework. We estimate our proposal on the motivating data that represent GPS locations of a Maremma Sheepdog recorded in Australia. The obtained results are easily interpretable and we show that our proposal outperforms the main competitive model

A multivariate circular-linear hidden Markov model for distributions-oriented wind forecast verication

Winds from the North-West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations in a residential neighborhood of the city of Taranto (Apulia, Italy). In 2012 the local government prescribed a reduction of industrial emissions by 10% every time such meteorological conditions are forecasted 72 hours in advance. Wind prediction is addressed using the Weather Research and Forecasting (WRF) atmospheric simulation system by the Regional Environmental Protection Agency (ARPA Puglia). In the framework of distributions-oriented forecast verication, we investigate the ability of the WRF system to properly predict the local wind speed and direction allowing dierent performances for unknown wind regimes. Ground-observed and WRF-predicted wind speed and direction at a relevant location are jointly modeled as a 4-dimensional time series with a nite number of states (wind regimes) characterized by homogeneous distributional behavior. Observed and simulated wind data are made of two circular (direction) and two linear (speed) variables, then the 4-dimensional time series is jointly modeled by a mixture of projected-skew normal distributions with time-independent states, where the temporal evolution of the state membership follows a rst order Markov process. Parameter estimates are obtained by a Bayesian MCMCbased method and results provide useful insights on wind regimes corresponding to dierent performances of WRF predictions

Circadian activity of small brown bear populations living in human-dominated landscapes

Whereas numerous studies on large carnivores have focused on analyzing spatial patterns and habitat use, the temporal dimension of their activity has been relatively little investigated, making this a topic of growing interest, especially in human-dominated landscapes. Relict and isolated Apennine brown bears (Ursus arctos marsicanus) have been living in a human-modified landscape since millennia, but no information is available on their activity patterns. By means of GPS telemetry (26,880 GPS locations collected from 18 adult Apennine brown bears) we investigated their circadian rhythms, using hourly movement rates as an index of bear activity. Based on a Bayesian modeling approach, circadian activity of Apennine brown bears was described by a bimodal curve, with peaks of activity around sunrise and sunset. We revealed seasonal effects, with bears exhibiting higher movement rates throughout the mating season, but no relevant influence of sex. In addition, bears increased their movement rate at distances < 100–500 m to roads and settlements exclusively during spring and late summer, suggesting a trade-off between foraging opportunities and risk avoidance. The absence of a marked nocturnality in Apennine brown bears suggests a relatively low degree of habitat encroachment and disturbance by humans. Yet, the occurrence of crepuscular activity patterns and the responses in proximity of anthropogenic landscape features likely indicate a coadaptation by bears to human presence through a shift in their temporal niche. Further studies should aim to unveil fitness implications of such modifications in activity patterns

EURECOM:Monthly Bulletin of European Community Economic and Financial News. July/August 1991 Vol. 3, No. 7

Winds from the North-West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations over a residential neighborhood in the city of Taranto (Italy). In 2012 the local government prescribed a reduction of industrial emissions by 10% every time such meteorological conditions are forecasted 72 hours in advance. Wind forecasting is addressed using the Weather Research and Forecasting (WRF) atmospheric simulation system by the Regional Environmental Protection Agency. In the context of distributions-oriented forecast verification, we propose a comprehensive modelbased inferential approach to investigate the ability of the WRF system to forecast the local wind speed and direction allowing different performances for unknown weather regimes. Ground-observed and WRF-forecasted wind speed and direction at a relevant location are jointly modeled as a 4-dimensional time series with an unknown finite number of states characterized by homogeneous distributional behavior. The proposed model relies on a mixture of joint projected and skew normal distributions with time-dependent states, where the temporal evolution of the state membership follows a first order Markov process. Parameter estimates, including the number of states, are obtained by a Bayesian MCMC-based method. Results provide useful insights on the performance of WRF forecasts in relation to different combinations of wind speed and direction