A fully nonparametric approach in survival models with explanatory variables

The most widely used model for multidimensional survival analysis is the Cox model. This model is semi-parametric, since its hazard function is the product of an unspecified baseline hazard, and a parametric functional form relating the hazard and the covariates. We consider a more flexible and fully nonparametric proportional hazards model, where the functional form of the covariates effect is left unspecified. In this model, estimation is based on the maximum likelihood method. Results obtained from a Monte-Carlo experiment and from real data are presented. Finally, the advantages and the limitations of the approacha are discussed.     

An Axiomatization of Cumulative Prospect Theory for Decision Under Risk

Cumulative prospect theory was introduced by Tversky and Kahneman so as to combine the empirical realism of their original prospect theory with the theoretical advantages of Quiggin's rank-dependent utility. Preference axiomatizations were provided in several papers. All those axiomatizations, however, only consider decision under uncertainty. No axiomatization has been provided as yet for decision under risk, i.e., the case in which given probabilities are transformed. Providing the latter is the purpose of this note. The resulting axiomatization is considerably simpler than that for uncertainty.     

Analysis of organizational conditions for risk management: the case study of a petrochemical site

This paper presents an ergonomic intervention in the petrochemical sector. The scheduled shutdown of one of the gas production sites has led the management to reduce the number of personnel on site, and then to get new recruits and experienced technicians from other sites as the policy for leaving personnel had not been properly planned, resulting in understaffing on site. Workers with seniority on the site, and who are also the most experienced do not accept the way newcomers are induced on site, whereas the management accuses them of resisting change. The intervention consisted in reconnecting local and corporate management through making the work activity visible and linking two sets of data that they held separately. Different types of analyses were made, work demography, decision making processes and tools used by the management, analysis of the building of career and work logics. Those different levels of analysis are gathered in macro-ergonomics, while showing the possible combinations between top down and bottom up approaches. The intervention resulted in concrete changes: HR simulation tool, training organisation, feedback.     

Preserving dominance relations through disaggregation: the evil and the saint

This article investigates the circumstances in which stochastic dominance relations at any finite degree at the household level can be assumed to be preserved at the individual level. We find necessary and sufficient conditions on the common sharing function adopted by households to divide the cake among a “strong” and a “weak” individual. The sharing function which maps the household income into the outcome of the weak individual must belong to the class of utility functions which supports the stochastic order. In addition, the household must follow a compensating rule, meaning that the share of resources devoted to the weak individual increases with household income. Applications to fiscal federalism are also proposed. “All inequality is a source of evil - for by the inferior more is lost in the account of happiness than is gained by the superior” J. Bentham, First Principle preparatory to Constitutional Code, 1822.      

Distribution asymptotique des autocorrélations ďun processus saisonnier non stationnaire

We examine the behaviour of the sample autocorrelations of a seasonal time series for which the first difference of order s (s ≥ 1) is stationary. The asymptotic distribution of the autocorrelations r'(k) based on uncentered data and of the autocorrelations r(k) based on centered data are derived. In each case, the asymptotic distribution is characterized as a function of the lag k and the parameters of the process. A simulation study was conducted in order to investigate the rate of convergence of the finite sample distributions of r(k) and r'(k) to their asymptotic counterparts and to evaluate the effect of centering or not centering the data on the distribution of autocorrelations.     

Analysing intangible resources and managing knowledge in a supply chain context

This paper reports the initial empirical work (and related findings) which has been carried out in a UK Government funded project titled `The Development of Partnership Strategies and the Management of Knowledge which Underpins Capabilities'. The companies collaborating in the research were Cellnet plc, Flymo Ltd and four of their strategic suppliers. This paper describes the validation of a technique for analysing the role of intangible resources in the development of partnership strategies. The technique codifies the tacit knowledge which a company's managers have of their company's strengths thereby enabling this understanding to be communicated to partners and used as a basis for joint strategic development. The paper goes on to outline a technique which is currently being tested for managing the knowledge associated with inter-organisational innovation.     

Model-based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis

Model-based clustering is a method that clusters data with an assumption of a statistical model structure. In this paper, we propose a novel model-based hierarchical clustering method for a finite statistical mixture model based on the Fisher distribution. The main foci of the proposed method are: (a) provide efficient solution to estimate the parameters of a Fisher mixture model (FMM); (b) generate a hierarchy of FMMs and (c) select the optimal model. To this aim, we develop a Bregman soft clustering method for FMM. Our model estimation strategy exploits Bregman divergence and hierarchical agglomerative clustering. Whereas, our model selection strategy comprises a parsimony-based approach and an evaluation graph-based approach. We empirically validate our proposed method by applying it on simulated data. Next, we apply the method on real data to perform depth image analysis. We demonstrate that the proposed clustering method can be used as a potential tool for unsupervised depth image analysis.