Dose‐Response Models Incorporating Aerosol Size Dependency for Francisella tularensis

The effect of bioaerosol size was incorporated into predictive dose‐response models for the effects of inhaled aerosols of Francisella tularensis (the causative agent of tularemia) on rhesus monkeys and guinea pigs with bioaerosol diameters ranging between 1.0 and 24 μm. Aerosol‐size‐dependent models were formulated as modification of the exponential and β‐Poisson dose‐response models and model parameters were estimated using maximum likelihood methods and multiple data sets of quantal dose‐response data for which aerosol sizes of inhaled doses were known. Analysis of F. tularensis dose‐response data was best fit by an exponential dose‐response model with a power function including the particle diameter size substituting for the rate parameter k scaling the applied dose. There were differences in the pathogen's aerosol‐size‐dependence equation and models that better represent the observed dose‐response results than the estimate derived from applying the model developed by the International Commission on Radiological Protection (ICRP, 1994) that relies on differential regional lung deposition for human particle exposure.     

An Extended Single‐index Model with Missing Response at Random

An extended single‐index model is considered when responses are missing at random. A three‐step estimation procedure is developed to define an estimator for the single‐index parameter vector by a joint estimating equation. The proposed estimator is shown to be asymptotically normal. An algorithm for computing this estimator is proposed. This algorithm only involves one‐dimensional nonparametric smoothers, thereby avoiding the data sparsity problem caused by high model dimensionality. Some simulation studies are conducted to investigate the finite sample performances of the proposed estimators.     

Multivariate log-skewed distributions with normal kernel and their applications

We introduce two classes of multivariate log-skewed distributions with normal kernel: the log canonical fundamental skew-normal (log-CFUSN) and the log unified skew-normal. We also discuss some properties of the log-CFUSN family of distributions. These new classes of log-skewed distributions include the log-normal and multivariate log-skew normal families as particular cases. We discuss some issues related to Bayesian inference in the log-CFUSN family of distributions, mainly we focus on how to model the prior uncertainty about the skewing parameter. Based on the stochastic representation of the log-CFUSN family, we propose a data augmentation strategy for sampling from the posterior distributions. This proposed family is used to analyse the US national monthly precipitation data. We conclude that a high-dimensional skewing function lead to a better model fit.     

Relations Between Alphabetized Name Order and Nomination Counts in Peer Nomination Measures

Peer nominations, a central method for measuring peer relationships in developmental research, typically involve asking children or adolescents to choose peers who fit various criteria from an alphabetized roster of classmates or grade‐mates. Although such measures have been used for decades, very little research has investigated the effects of alphabetical name order on the number of nominations received by peers. This study collected peer nominations for 20 items among 607 eighth grade participants in two schools. Regression analyses showed that earlier name order significantly predicted higher nomination counts for eight of the items, and explained over 5 percent of the variance in four affective variables (friendship, acceptance, acquaintanceship, and received liking). Across variables, name order effects were negatively correlated with internal reliability of nominations, implying that order effects may be related to the consensus of the peer group. Name order also had a minimal effect on inter‐correlations among a subset of variables. Implications and concrete recommendations for controlling and reducing name order effects in future research are discussed.     

The Relation Between Therapeutic Alliance and Treatment Motivation in Residential Youth Care: A Cross-Lagged Panel Analysis

This study investigated the longitudinal relation between therapeutic alliance and treatment motivation in a sample of 174 adolescents receiving residential treatment in the Netherlands. Structural equation modeling with a cross-lagged panel design was used to examine the relation between therapeutic alliance and treatment motivation up to 9 months of treatment. Results revealed that autoregressive associations between initial therapeutic alliance and alliance at subsequent time points were significant, whereas for treatment motivation a significant association was found after 6 months, but not after 9 months. Results also showed that a higher level of therapeutic alliance after 3 months was predictive of a higher level of treatment motivation after 6 months. Furthermore, a higher level of therapeutic alliance after 6 months was predictive of a higher level of treatment motivation after 9 months. Implications for research and clinical practice are discussed.     

Some Results Based on a Version of the Generalized Dynamic Entropies

We propose new dynamic measures of uncertainty based on the notion of generalized dynamic entropy introduced in Di Crescenzo and Longobardi (2006). These can uniquely determine distribution functions in continuous and discrete cases, and the characterizations of some well-known distributions are provided. We also define some orderings and aging notions based on the generalized dynamic measures, and prove some of their properties, obtaining as corollaries results that have recently appeared in the literature.     

Longitudinal Links between Adolescent and Peer Conduct Problems and Moderation by a Sensitivity Genetic Index

The most extensively studied influence on adolescent conduct problem behaviors is peers, and the literature points to genetics as one source of individual differences in peer influence. The purpose of this study was to test the hypothesis that an environmental sensitivity genetic index comprised of DRD4, 5‐HTTLPR, and GABRA2 variation would moderate the association between peer and adolescent conduct problems. Latent growth modeling was applied to PROSPER project longitudinal data from adolescents and their peers. Results showed the hypothesis was supported; adolescents with more copies of putative sensitivity alleles were more strongly influenced by their peers. The interaction form was consistent with differential susceptibility in follow‐up analyses. Strengths and weaknesses of genetic aggregates for sensitivity research are discussed.     

Identifying Changing Aviation Threat Environments Within an Adaptive Homeland Security Advisory System

A critical component of aviation security consists of screening passengers and baggage to protect airports and aircraft from terrorist threats. Advancements in screening device technology have increased the ability to detect these threats; however, specifying the operational configurations of these devices in response to changes in the threat environment can become difficult. This article proposes to use Fisher information as a statistical measure for detecting changes in the threat environment. The perceived risk of passengers, according to prescreening information and behavior analysis, is analyzed as the passengers sequentially enter the security checkpoint. The alarm responses from the devices used to detect threats are also analyzed to monitor significant changes in the frequency of threat items uncovered. The key results are that this information‐based measure can be used within the Homeland Security Advisory System to indicate changes in threat conditions in real time, and provide the flexibility of security screening detection devices to responsively and automatically adapt operational configurations to these changing threat conditions.