Longitudinal Associations Between Other‐Sex Friendships and Substance Use in Adolescence

The impact of the changes in the gender composition of friendship networks during early adolescence on substance use in late adolescence was examined. The hypothesis was that initial level and increase in the proportion of other‐sex friends in the network would be associated with higher levels of substance use among girls, but not among boys. Girls and boys (n=390) were interviewed annually from ages 12 to 18 (79% retention). For both boys and girls, initial level in the proportion of other‐sex friends predicted alcohol use in late adolescence, whereas it was predictive of drug use in girls only. Moreover, for girls only, a faster increase in the proportion of other‐sex friends in the network predicted later use of alcohol and drugs.     

Information bounds and MCMC parameter estimation for the pile-up model

This paper is concerned with the pile-up model defined as a nonlinear transformation of a distribution of interest. An observation of the pile-up model is the minimum of a random number of independent variables from the distribution of interest. One specific pile-up model is encountered in time-resolved fluorescence where only the first photon of a random number of photons is observed. In the first part of the paper the Cramér-Rao bound is studied to optimize the experimental conditions by choosing the best tuning parameter which is the average number of variables over which the minimum is taken. The implication is that the tuning parameter currently used in fluorescence does not minimize the acquisition time. However, data obtained at the optimal choice of the tuning parameter require an estimator adapted to the pile-up effect, therefore, an appropriate Gibbs sampler is presented. The covariance matrix of this estimator turns out to be close to the Cramér-Rao bound and hence the acquisition time may be reduced considerably.     

Bayesian Development of a Dose‐Response Model for Aspergillus fumigatus and Invasive Aspergillosis

Invasive aspergillosis (IA) is a major cause of mortality in immunocompromized hosts, most often consecutive to the inhalation of spores of Aspergillus. However, the relationship between Aspergillus concentration in the air and probability of IA is not quantitatively known. In this study, this relationship was examined in a murine model of IA. Immunosuppressed Balb/c mice were exposed for 60 minutes at day 0 to an aerosol of A. fumigatus spores (Af293 strain). At day 10, IA was assessed in mice by quantitative culture of the lungs and galactomannan dosage. Fifteen separate nebulizations with varying spore concentrations were performed. Rates of IA ranged from 0% to 100% according to spore concentrations. The dose‐response relationship between probability of infection and spore exposure was approximated using the exponential model and the more flexible beta‐Poisson model. Prior distributions of the parameters of the models were proposed then updated with data in a Bayesian framework. Both models yielded close median dose‐responses of the posterior distributions for the main parameter of the model, but with different dispersions, either when the exposure dose was the concentration in the nebulized suspension or was the estimated quantity of spores inhaled by a mouse during the experiment. The median quantity of inhaled spores that infected 50% of mice was estimated at 1.8 × 10⁴ and 3.2 × 10⁴ viable spores in the exponential and beta‐Poisson models, respectively. This study provides dose‐response parameters for quantitative assessment of the relationship between airborne exposure to the reference A. fumigatus strain and probability of IA in immunocompromized hosts.     

A Communication Perspective on Video Lottery Terminals

Video Lottery Terminals (VLT) are associated with pathological gambling and with most of the requests for help in combating gambling addiction. Embeddedness of a person in his or her social network is among the communicational factors that may help explain this phenomenon. To verify this, we compared ego networks of VLT gamblers, of gamblers of games with low request for help and of VLT gamblers in treatment (n = 90). The networks of regular VLT gamblers are small and dense and offer little social support. Gamblers in treatment also have small networks, but they are less dense, have more components and offer more social support. Networks of gamblers with low requests for assistance are approximately twice the size as those of VLT gamblers, are sparser and offer more companionship. In conclusion, the VLT gambler is not an isolated individual, but rather an individual ‘shut-in’ a small network of tightly knitted relationships.     

About an adaptively weighted Kaplan-Meier estimate

The minimum averaged mean squared error nonparametric adaptive weights use data from m possibly different populations to infer about one population of interest. The definition of these weights is based on the properties of the empirical distribution function. We use the Kaplan-Meier estimate to let the weights accommodate right-censored data and use them to define the weighted Kaplan-Meier estimate. The proposed estimate is smoother than the usual Kaplan-Meier estimate and converges uniformly in probability to the target distribution. Simulations show that the performances of the weighted Kaplan-Meier estimate on finite samples exceed that of the usual Kaplan-Meier estimate. A case study is also presented.     

Tax–benefit revealed social preferences

This paper inverts the usual logic of applied optimal income taxation. It starts from the observed distribution of income before and after redistribution and corresponding marginal tax rates. Under a set of simplifying assumptions, it is then possible to recover the social welfare function that would make the observed marginal tax rate schedule optimal. In this framework, the issue of the optimality of an existing tax–benefit system is transformed into the issue of the shape of the social welfare function associated with that system and whether it satisfies elementary properties. This method is applied to the French redistribution system with the interesting implication that the French redistribution authority may appear, under some plausible scenario concerning the size of the labor supply behavioral reactions, non Paretian (e.g. giving negative marginal social weights to the richest class of tax payers).     

Asymptotic properties of the MAMSE adaptive likelihood weights

The weighted likelihood is a generalization of the likelihood designed to borrow strength from similar populations while making minimal assumptions. If the weights are properly chosen, the maximum weighted likelihood estimate may perform better than the maximum likelihood estimate (MLE). In a previous article, the minimum averaged mean squared error (MAMSE) weights are proposed and simulations show that they allow to outperform the MLE in many cases. In this paper, we study the asymptotic properties of the MAMSE weights. In particular, we prove that the MAMSE-weighted mixture of empirical distribution functions converges uniformly to the target distribution and that the maximum weighted likelihood estimate is strongly consistent. A short simulation illustrates the use of bootstrap in this context.     

Using Predictive Risk for Process Control

When monitoring highly capable processes, it is often desirable to tolerate small instabilities in order to avoid tempering. One approach in this setting is to monitor the capability of the process dynamically and signal if the estimated capability reaches an unacceptably low level. We suggest that monitoring the probability of the next item not falling between the specification limits is a more natural scale to evaluate risk, and offers appreciable flexibility. We use a statistical model and a window of data to evaluate this probability and decide if the process should be halted immediately based on that estimate. The properties of this method are explored numerically and a case study is provided.     

Information criteria for inhomogeneous spatial point processes

The theoretical foundation for a number of model selection criteria is established in the context of inhomogeneous point processes and under various asymptotic settings: infill, increasing domain and combinations of these. For inhomogeneous Poisson processes we consider Akaike's information criterion and the Bayesian information criterion, and in particular we identify the point process analogue of ‘sample size’ needed for the Bayesian information criterion. Considering general inhomogeneous point processes we derive new composite likelihood and composite Bayesian information criteria for selecting a regression model for the intensity function. The proposed model selection criteria are evaluated using simulations of Poisson processes and cluster point processes.