The Economic Impact of Space Weather: Where Do We Stand?

Space weather describes the way in which the Sun, and conditions in space more generally, impact human activity and technology both in space and on the ground. It is now well understood that space weather represents a significant threat to infrastructure resilience, and is a source of risk that is wide‐ranging in its impact and the pathways by which this impact may occur. Although space weather is growing rapidly as a field, work rigorously assessing the overall economic cost of space weather appears to be in its infancy. Here, we provide an initial literature review to gather and assess the quality of any published assessments of space weather impacts and socioeconomic studies. Generally speaking, there is a good volume of scientific peer‐reviewed literature detailing the likelihood and statistics of different types of space weather phenomena. These phenomena all typically exhibit “power‐law” behavior in their severity. The literature on documented impacts is not as extensive, with many case studies, but few statistical studies. The literature on the economic impacts of space weather is rather sparse and not as well developed when compared to the other sections, most probably due to the somewhat limited data that are available from end‐users. The major risk is attached to power distribution systems and there is disagreement as to the severity of the technological footprint. This strongly controls the economic impact. Consequently, urgent work is required to better quantify the risk of future space weather events.     

Individual Prior Information in a Physiological Model of 2H8-Toluene Kinetics: An Empirical Bayes Estimation Strategy

Physiologically-based toxicokinetic (PBTK) models are widely used to quantify whole-body kinetics of various substances. However, since they attempt to reproduce anatomical structures and physiological events, they have a high number of parameters. Their identification from kinetic data alone is often impossible, and other information about the parameters is needed to render the model identifiable. The most commonly used approach consists of independently measuring, or taking from literature sources, some of the parameters, fixing them in the kinetic model, and then performing model identification on a reduced number of less certain parameters. This results in a substantial reduction of the degrees of freedom of the model. In this study, we show that this method results in final estimates of the free parameters whose precision is overestimated. We then compared this approach with an empirical Bayes approach, which takes into account not only the mean value, but also the error associated with the independently determined parameters. Blood and breath ²H₈-toluene washout curves, obtained in 17 subjects, were analyzed with a previously presented PBTK model suitable for person-specific dosimetry. Model parameters with the greatest effect on predicted levels were alveolar ventilation rate Q_PC, fat tissue fraction V_FC, blood-air partition coefficient K_b, fraction of cardiac output to fat Q_a/co and rate of extrahepatic metabolism V_max-p. Differences in the measured and Bayesian-fitted values of Q_PC, V_FC and K_b were significant (p < 0.05), and the precision of the fitted values V_max-p and Q_a/co went from 11 ± 5% to 75 ± 170% (NS) and from 8 ± 2% to 9 ± 2% (p < 0.05) respectively. The empirical Bayes approach did not result in less reliable parameter estimates: rather, it pointed out that the precision of parameter estimates can be overly optimistic when other parameters in the model, either directly measured or taken from literature sources, are treated as known without error. In conclusion, an empirical Bayes approach to parameter estimation resulted in a better model fit, different final parameter estimates, and more realistic parameter precisions.     

Explaining Family Change and Variation: Challenges for Family Demographers

Twenty years ago, the National Institute of Child Health and Human Development (NICHD) issued a request for proposals that resulted in the National Survey of Families and Households (NSFH), a unique survey valuable to a wide range of family scholars. This paper describes the efforts of an interdisciplinary group of family demographers to build on the progress enabled by the NSFH and many other theoretical and methodological innovations. Our work, also supported by NICHD, will develop plans for research and data collection to address the central question of what causes family change and variation. We outline the group's initial assessments of orienting frameworks, key aspects of family life to study, and theoretical and methodological challenges for research on family change. Finally, we invite family scholars to follow our progress and to help develop this shared public good.     

E-optimal design in irregular BIBD settings

When the necessary conditions for a BIBD are satisfied, but no BIBD exists, there is no simple answer for the optimal design problem. This paper identifies the E-optimal information matrices for any such irregular BIBD setting when the number of treatments is no larger than 100. A- and D-optimal designs are typically not E-optimal. An E-optimal design for 15 treatments in 21 blocks of size 5 is found.     

Pathways to parenting stress reduction among parents in South Africa

Parenting stress has a range of effects on parents and their children. Despite existing evidence on the effectiveness of family-based interventions on reducing parenting stress, little is known about the mechanism of change that contributes to its reduction. This study investigates the mechanism of change in a parenting programme (Parenting for Lifelong Health [PLH]) on reducing parenting stress among parents of adolescents in South Africa. A pragmatic cluster randomized controlled trial was conducted among a total sample of 552 parents and primary caregivers (aged, M = 49.37; SD = 14.69) who were recruited from 40 communities in South Africa's Eastern Cape Province. A mediation analysis was performed to investigate direct and indirect effects using PROCESS macrostatistical software. The findings of the study indicate that parenting stress reduction operates via three significant mediators: improved parent–child relationship (β = 0.058, P < 0.000), reduced parental depression (β = −0.103, P < 0.000) and reduced family financial strain (β = −0.049, P < 0.000). The findings of the study highlight the importance of considering child, parental and contextual factors in the design and development of interventions aimed at reducing parenting stress in families in low- and middle-income settings.     

CHOICE-CONSTRAINED CONJOINT ANALYSIS

Choice-constrained conjoint analysis (CCCA) is a new method for metric conjoint analysis studies. It computes part-worth utility functions that account for “revealed preference”—those products a respondent actually selects in an independent choice situation. CCCA uses an iterative penalty function estimation procedure that successively modifies initial regressionderived part worths so that respondent choices (either actual or intended) of real brands are predicted as accurately as possible. The paper first describes the motivation and rationale for CCCA and presents the mathematics of the algorithm. As an illustration, it applies the CCCA model and penalty function estimation procedure to a limited set of synthetic data. A second application of the technique is presented that uses data obtained by a major telecommunications firm that used conjoint analysis to examine the importance of several features of residential communication devices. The paper also discusses potential extensions of the CCCA model and the kinds of marketing applications for which it might be useful.     

REPLY TO: “ON THE SENSITIVITY OF BRAND-CHOICE SIMULATIONS TO ATTRIBUTE IMPORTANCE WEIGHTS”

Our reply to Curry, Louviere, and Augustine's critique of our earlier paper focuses on differences in motivation between our research and theirs. Our interest in the problem relates to the possible incorporation of self-explicated evaluations in conjoint data collection methods; subsequent to the appearance of our original paper, we have developed hybrid models that combine elements of self-explicated (compositional) and conjoint (decompositional) data collection procedures. As far as we can surmise from their critique, Curry, Louviere, and Augustine are concerned with much broader strategic issues relating share of choices in the consumer population to changes in the shape of attribute weight distributions, shape of the Pareto tradeoff boundary, and so on.     

The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models

Summary.  We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.