On Business Analytics: Dynamic Network Analysis for Descriptive Analytics and Multicriteria Decision Analysis for Prescriptive Analytics.

Ferry Jules. Collèges communaux. — Classement des professeurs. In: Bulletin administratif de l'instruction publique. Tome 24 n°467, 1881. pp. 836-842

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

SIS 2017. Statistics and Data Science: new challenges, new generations

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data

A review of commercialisation mechanisms for carbon dioxide removal

The deployment of carbon dioxide removal (CDR) needs to be scaled up to achieve net zero emission pledges. In this paper we survey the policy mechanisms currently in place globally to incentivise CDR, together with an estimate of what different mechanisms are paying per tonne of CDR, and how those costs are currently distributed. Incentive structures are grouped into three structures, market-based, public procurement, and fiscal mechanisms. We find the majority of mechanisms currently in operation are underresourced and pay too little to enable a portfolio of CDR that could support achievement of net zero. The majority of mechanisms are concentrated in market-based and fiscal structures, specifically carbon markets and subsidies. While not primarily motivated by CDR, mechanisms tend to support established afforestation and soil carbon sequestration methods. Mechanisms for geological CDR remain largely underdeveloped relative to the requirements of modelled net zero scenarios. Commercialisation pathways for CDR require suitable policies and markets throughout the projects development cycle. Discussion and investment in CDR has tended to focus on technology development. Our findings suggest that an equal or greater emphasis on policy innovation may be required if future requirements for CDR are to be met. This study can further support research and policy on the identification of incentive gaps and realistic potential for CDR globally