Markov-switching generalized additive models

We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain. Building on the powerful hidden Markov model machinery and the methods for penalized B-splines routinely used in regression analyses, we develop a framework for nonparametrically estimating the functional form of the effect of the covariates in such a regression model, assuming an additive structure of the predictor. The resulting class of Markov-switching generalized additive models is immensely flexible, and contains as special cases the common parametric Markov-switching regression models and also generalized additive and generalized linear models. The feasibility of the suggested maximum penalized likelihood approach is demonstrated by simulation and further illustrated by modelling how energy price in Spain depends on the Euro/Dollar exchange rate

La GIZC à la lumière du principe de responsabilités communes mais différenciées : La coopération internationale en perspective

La GIZC s’impose comme un processus de recomposition des territoires et comme un « modèle opérationnel » de gouvernance environnementale. Les zones côtières étant particulièrement vulnérables aux atteintes et perturbations des écosystèmes, leur gestion pose avec acuité la question des responsabilités et de l’efficacité de la coopération internationale. L’application du principe de responsabilités communes mais différenciées (PCRD) qui repose sur un socle juridique commun avec la GIZC dans le cadre de la mise en place d’un partenariat environnemental mondial établi lors de la Conférence de Rio de 1992, participe d’une réflexion sur la gestion durable des zones côtières dans une perspective de responsabilité morale et juridique. Il permet de repositionner la GIZC dans la coopération internationale. La réalisation des objectifs de la GIZC sont par ailleurs en convergence avec les orientations données par le PCRD sous l’angle de la gouvernance environnementale internationale. La GIZC conduit les États, les autorités infra-étatiques et la Communauté internationale à assumer des responsabilités communes mais différenciées dans le domaine de la gestion des zones côtières. Fondée sur les approches de l’intégration, de l’interdépendance et de la participation, la GIZC permet de donner une application concrète du PRCD en dépassant la conception traditionnelle de la territorialité et donc en surmontant certaines limites d’interprétation et d’application du PRCD. Elle permet d’envisager l’émergence d’un nouvel ordre territorial mondial.The integrated coastal management (ICM) asserts itself as a process of territorial recomposition and as an operational model of environmental governance. Coastal areas are especially vulnerable to ecosystems disturbances and disruptions so their management is questioning responsibilities and efficiency of international cooperation. The principle of common but differenciated responsibilities (PCDR) based on similar legal references with ICM in accordance with the Global Environmental Partnership established at 1992 Rio Conference, participates to develop a reflection on sustainable management of coastal areas related to moral and judicial responsibility. The PCDR put into perspective ICM in international cooperation. The making of ICM objectives are converging to PCDR orientations concerning international environmental governance. ICM conducts States, national authorities and International community to assume common but differenciated responsibilities in the field of coastal management. Based on integration, inderdependancy and participation, ICM gives a concrete implementation of the PCDR, surpassing the traditional conception of territoriality and then overcoming limits of the principle. It leads to consider the emergence of a new global territorial order

hmmTMB: Hidden Markov models with flexible covariate effects in R

Hidden Markov models (HMMs) are widely applied in studies where a discrete-valued process of interest is observed indirectly. They have for example been used to model behaviour from human and animal tracking data, disease status from medical data, and financial market volatility from stock prices. The model has two main sets of parameters: transition probabilities, which drive the latent state process, and observation parameters, which characterise the state-dependent distributions of observed variables. One particularly useful extension of HMMs is the inclusion of covariates on those parameters, to investigate the drivers of state transitions or to implement Markov-switching regression models. We present the new R package hmmTMB for HMM analyses, with flexible covariate models in both the hidden state and observation parameters. In particular, non-linear effects are implemented using penalised splines, including multiple univariate and multivariate splines, with automatic smoothness selection. The package allows for various random effect formulations (including random intercepts and slopes), to capture between-group heterogeneity. hmmTMB can be applied to multivariate observations, and it accommodates various types of response data, including continuous (bounded or not), discrete, and binary variables. Parameter constraints can be used to implement non-standard dependence structures, such as semi-Markov, higher-order Markov, and autoregressive models. Here, we summarise the relevant statistical methodology, we describe the structure of the package, and we present an example analysis of animal tracking data to showcase the workflow of the package

Forensic intelligence : applications in illegal drug trafficking

University of Technology Sydney. Faculty of Science.This research aimed at getting a better understanding of illicit drug trafficking, especially from an Australian point of view, by looking at different approaches of getting valuable information in a timely fashion for forensic intelligence purpose. The study was conducted in collaboration with the Australian Federal Police (AFP) who provided appropriate data. In return, the study was expected to provide findings to grow their knowledge about such criminal phenomenon that is illegal drug trafficking. Two distinct approaches were undertaken. The first one was an analysis of chemical results of cocaine and heroin border seizures performed by the AFP during 2008 and 2013. Trends regarding the purity as well as added compounds over time and per geographic location were discovered. Moreover, statistical methods were applied on the provided datasets to assess the feasibility to develop an automatic triage of those chemical results and highlighting links between seizures based on their chemical data. Promising results with few error rates were obtained, as cocaine seizures could be discriminated with 9.36 % of false positives and 2.45 % of false negatives, and heroin seizures could be discriminated with 4.82 % of false positives and 2.94 % of false negatives. Therefore, the automatic statistical model could be implemented for routine use at the AFP. The second approach was a proof of concept study investigating the possibility to use currently deployed portable instruments for intelligence purpose instead of the traditional identification and case-specific aim that they are designed for. Three different technologies were tested, Attenuated Total Reflectance - Transform Infrared spectroscopy (ATR-FTIR), Ion Mobility Spectroscopy (IMS) and Ion trap tandem Mass Spectrometry with Atmospheric Pressure Chemical Ionization (APCI-ITMS-MS) for the detection of remnants of drugs present on the surface of passports, using various parameters including transfer, activity and persistence. An experimental design was developed and different scenarios were trialled. Promising results were obtained especially with APCI-ITMS-MS, as drugs’ residues could be detected even after an activity of thirty minutes in quantities less than 0.05 μg. The findings demonstrate that a routine use at customs would be feasible to obtain a better overview of trafficking flows instead of targeting specific individuals. The different projects conducted within this research emphasise the need for data triangulation and using various source of information to get a more holistic view of the criminality, in this case illegal drug trafficking

Sets of efficient points in a normed space

AbstractSets of efficient points in a normed space with respect to the distances to the points of a given compact set are geometrically characterized; hull and closure properties are obtained. These results are relevant to geometry of normed spaces and are mostly useful in the context of location theory

momentuHMM: R package for generalized hidden Markov models of animal movement

1. Discrete‐time hidden Markov models (HMMs) have become an immensely popular tool for inferring latent animal behaviours from telemetry data. While movement HMMs typically rely solely on location data (e.g. step length and turning angle), auxiliary biotelemetry and environmental data are powerful and readily‐available resources for incorporating much more ecological and behavioural realism. However, complex movement or observation process models often necessitate custom and computationally demanding HMM model‐fitting techniques that are impractical for most practitioners, and there is a paucity of generalized user‐friendly software available for implementing multivariate HMMs of animal movement. 2. Here, we introduce an open‐source R package, momentuHMM, that addresses many of the deficiencies in existing HMM software. Features include: (1) data pre‐processing and visualization; (2) user‐specified probability distributions for an unlimited number of data streams and latent behaviour states; (3) biased and correlated random walk movement models, including dynamic “activity centres” associated with attractive or repulsive forces; (4) user‐specified design matrices and constraints for covariate modelling of parameters using formulas familiar to most R users; (5) multiple imputation methods that account for measurement error and temporally irregular or missing data; (6) seamless integration of spatio‐temporal covariate raster data; (7) cosinor and spline models for cyclical and other complicated patterns; (8) model checking and selection; and (9) simulation. 3. After providing an overview of the main features of the package, we demonstrate some of the capabilities of momentuHMM using real‐world examples. These include models for cyclical movement patterns of African elephants, foraging trips of northern fur seals, loggerhead turtle movements relative to ocean surface currents, and grey seal movements among three activity centres. 4. momentuHMM considerably extends the capabilities of existing HMM software while accounting for common challenges associated with telemetry data. It therefore facilitates more realistic hypothesis‐driven animal movement analyses that have hitherto been largely inaccessible to non‐statisticians. While motivated by telemetry data, the package can be used for analysing any type of data that is amenable to HMMs. Practitioners interested in additional features are encouraged to contact the authors