Modelling Aggregation on the Large Scale and Regularity on the Small Scale in Spatial Point Pattern Datasets

We consider a dependent thinning of a regular point process with the aim of obtaining aggregation on the large scale and regularity on the small scale in the resulting target point process of retained points. Various parametric models for the underlying processes are suggested and the properties of the target point process are studied. Simulation and inference procedures are discussed when a realization of the target point process is observed, depending on whether the thinned points are observed or not. The paper extends previous work by Dietrich Stoyan on interrupted point processes.     

Interspecies Extrapolation: A Reexamination of Acute Toxicity Data

We reanalyze the acute toxicity data on cancer chemotherapeutic agents compiled by Freireich et al.(1) and Schein et al.(2) to derive coefficients of the allometric equation for scaling toxic doses across species (toxic dose = a.[body weight]b). In doing so, we extend the analysis of Travis and White (Risk Analysis, 1988, 8, 119-125) by addressing uncertainties inherent in the analysis and by including the hamster data, previously not used. Through Monte Carlo sampling, we specifically account for measurement errors when deriving confidence intervals and testing hypotheses. Two hypotheses are considered: first, that the allometric scaling power (b) varies for chemicals of the type studied; second, that the same scaling power, or "scaling law," holds for all chemicals in the data set. Following the first hypothesis, in 95% of the cases the allometric power of body weight falls in the range from 0.42-0.97, with a population mean of 0.74. Assuming the second hypothesis to be true-that the same scaling law is followed for all chemicals-the maximum likelihood estimate of the scaling power is 0.74; confidence bounds on the mean depend on the size of measurement error assumed. Under a "best case" analysis, 95% confidence bounds on the mean are 0.71 and 0.77, similar to the results reported by Travis and White. For alternative assumptions regarding measurement error, the confidence intervals are larger and include 0.67, but not 1.00. Although a scaling power of about 0.75 provides the best fit to the data as a whole, a scaling power of 0.67, corresponding to scaling per unit surface area, is not rejected when the nonhomogeneity of variances is taken into account. Hence, both surface area and 0.75 power scaling are consistent with the Freireich et al. and Schein et al. data sets. To illustrate the potential impact of overestimating the scaling power, we compare reported human MTDs to values extrapolated from mouse LD10s.     

Testing for residual correlation of any order in the autoregressive process

We are interested in the implications of a linearly autocorrelated driven noise on the asymptotic behavior of the usual least-squares estimator in a stable autoregressive process. We show that the least-squares estimator is not consistent and we suggest a sharp analysis of its almost sure limiting value as well as its asymptotic normality. We also establish the almost sure convergence and the asymptotic normality of the estimated serial correlation parameter of the driven noise. Then, we derive a statistical procedure enabling to test for correlation of any order in the residuals of an autoregressive modelling, giving clearly better results than the commonly used portmanteau tests of Ljung–Box and Box–Pierce, and appearing to outperform the Breusch–Godfrey procedure on small-sized samples.     

A multi-user tool for enhancing the daily replanning and control of visits in home care services

The uncertainty associated with patients’ demands and operators’ availabilities is the main complexity factor in planning health care activities. Thus, activity replanning is required to react to the variations and to guarantee a good service quality. A particularly complex replanning activity is required in Home Care (HC) services, where any variation to the plans involves several decisions related, e.g. to nurses’ travels. Such complexity makes impossible for planners to integrate and to effectively handle all of the information in real time with the classical HC management tools. In this work, we design, implement and validate a new visualisation tool to support HC planners in handling the data, to better perform their replanning activities. The tool consists of a desk equipped with an interactive map, where up to four planners can manage the information together in a multilayer configuration. This solution allows to manage the high amount of data in a more effective and natural way than the tabular form of the current commercial tools, due to both the horizontal map visualisation and the multilayer information provisioning. The prototype has been tested with real HC planners and expert users; outcomes show the capability to meet the gap between planners and information complexity, and to provide adequate support for replanning HC activities.     

A Gendered Assessment of Highly Skilled Emigration

Although women form a large and increasing proportion of international migrants, women's mobility has generally been overlooked in the literature. Quantifying and characterizing female migration should lead to a better understanding of the forces that shape international migration. We build an original data set providing gender‐disaggregated indicators of international migration by educational attainment for 195 source countries in 1990 and 2000. We find that women represent an increasing share of the immigration stock in the OECD countries and exhibit higher skilled emigration rates than men.     

THE DURATION OF FIRM‐DESTINATION EXPORT RELATIONSHIPS: EVIDENCE FROM SPAIN, 1997–2006

This article uses survival analysis to investigate the duration of Spanish firms' trade relationships by destination over 1997–2006. Whereas firm export status is highly persistent, firms' destination portfolio is very dynamic: a typical firm‐country exporting relationship has a median duration of 2 years. Yet, if a firm manages to export to a country beyond 2 years the risk of exiting that market sharply falls afterwards. The results indicate that not only firm heterogeneity but also destination heterogeneity are crucial to explain survival in export markets. In particular, country (political) risk heavily shapes the effect of firm, product, and other destination characteristics on the length of trade relationships. Whereas firm productivity, comparative advantage, partners' GDP, and proximity enhance duration of trade with low‐risk countries, they have no effect on trade survival with high‐risk countries. On the contrary, information spillovers are particularly relevant to enhance survival of trade relationships with high‐risk countries. (JEL C41, F10, F14)     

Hazard Perception, Risk Perception, and the Need for Decontamination by Residents Exposed to Soil Pollution: The Role of Sustainability and the Limits of Expert Knowledge

This case study examines the hazard and risk perception and the need for decontamination according to people exposed to soil pollution. Using an ecological-symbolic approach (ESA), a multidisciplinary model is developed that draws upon psychological and sociological perspectives on risk perception and includes ecological variables by using data from experts' risk assessments. The results show that hazard perception is best predicted by objective knowledge, subjective knowledge, estimated knowledge of experts, and the assessed risks. However, experts' risk assessments induce an increase in hazard perception only when residents know the urgency of decontamination. Risk perception is best predicted by trust in the risk management. Additionally, need for decontamination relates to hazard perception, risk perception, estimated knowledge of experts, and thoughts about sustainability. In contrast to the knowledge deficit model, objective and subjective knowledge did not significantly relate to risk perception and need for decontamination. The results suggest that residents can make a distinction between hazards in terms of the seriousness of contamination on the one hand, and human health risks on the other hand. Moreover, next to the importance of social determinants of environmental risk perception, this study shows that the output of experts' risk assessments—or the objective risks—can create a hazard awareness rather than an alarming risk consciousness, despite residents' distrust of scientific knowledge.     

Adaptive methods for sequential importance sampling with application to state space models

In this paper we discuss new adaptive proposal strategies for sequential Monte Carlo algorithms—also known as particle filters—relying on criteria evaluating the quality of the proposed particles. The choice of the proposal distribution is a major concern and can dramatically influence the quality of the estimates. Thus, we show how the long-used coefficient of variation (suggested by Kong et al. in J. Am. Stat. Assoc. 89(278–288):590–599, 1994) of the weights can be used for estimating the chi-square distance between the target and instrumental distributions of the auxiliary particle filter. As a by-product of this analysis we obtain an auxiliary adjustment multiplier weight type for which this chi-square distance is minimal. Moreover, we establish an empirical estimate of linear complexity of the Kullback-Leibler divergence between the involved distributions. Guided by these results, we discuss adaptive designing of the particle filter proposal distribution and illustrate the methods on a numerical example. This work was partly supported by the National Research Agency (ANR) under the program “ANR-05-BLAN-0299”.