A robust prediction error criterion for pareto modelling of upper tails

Estimation of the Pareto tail index from extreme order statistics is an important problem in many settings. The upper tail of the distribution, where data are sparse, is typically fitted with a model, such as the Pareto model, from which quantities such as probabilities associated with extreme events are deduced. The success of this procedure relies heavily not only on the choice of the estimator for the Pareto tail index but also on the procedure used to determine the number k of extreme order statistics that are used for the estimation. The authors develop a robust prediction error criterion for choosing k and estimating the Pareto index. A Monte Carlo study shows the good performance of the new estimator and the analysis of real data sets illustrates that a robust procedure for selection, and not just for estimation, is needed.     

Community Wildfire Events as a Source of Social Conflict*

Abstract The literature notes that natural disasters, including wildfires, that damage human settlements often have the short‐term effect of “bringing people together.” Less recognized is the fact that such events can also generate social conflict at the local level. This study examines the specific sources of such social conflict during and after community wildfire events. Examining qualitative data generated from six case studies of wildfires in the American West, we suggest that integrating the theories of Weber, Giddens, and Habermas with community interaction theory provides a context for understanding such conflict. Rationalized forms of interaction and problem solving imposed by extra‐local organizations during and after wildfire events are often resisted by local actors who are also inhibited from acting due to local capacity limitations. Thus, conflict occurs when social relations are disembedded by non‐local entities, and there is a perceived loss of local agency.     

Statistical Distributions of Daily Breathing Rates for Narrow Age Groups of Infants and Children

Children may be more susceptible to toxicity from some environmental chemicals than adults. This susceptibility may occur during narrow age periods (windows), which can last from days to years depending on the toxicant. Breathing rates specific to narrow age periods are useful to assess inhalation dose during suspected windows of susceptibility. Because existing breathing rates used in risk assessment are typically for broad age ranges or are based on data not representative of the population, we derived daily breathing rates for narrow age ranges of children designed to be more representative of the current U.S. children's population. These rates were derived using the metabolic conversion method of Layton (1993) and energy intake data adjusted to represent the U.S. population from a relatively recent dietary survey (CSFII 1994–1996, 1998). We calculated conversion factors more specific to children than those previously used. Both nonnormalized (L/day) and normalized (L/kg-day) breathing rates were derived and found comparable to rates derived using energy estimates that are accurate for the individuals sampled but not representative of the population. Estimates of breathing rate variability within a population can be used with stochastic techniques to characterize the range of risk in the population from inhalation exposures. For each age and age-gender group, we present the mean, standard error of the mean, percentiles (50th, 90th, and 95th), geometric mean, standard deviation, 95th percentile, and best-fit parametric models of the breathing rate distributions. The standard errors characterize uncertainty in the parameter estimate, while the percentiles describe the combined interindividual and intra-individual variability of the sampled population. These breathing rates can be used for risk assessment of subchronic and chronic inhalation exposures of narrow age groups of children.     

The Devil is in the Detail: Clinicians,Academics and Researchers Resolving the Problems of Clinical Research

The difficulties associated with conducting valid family therapy research within a clinical practice discourage many potential researchers. This article will describe collaboration between a group of academics, researchers and clinicians who decided to explore the process and efficacy of systemic family therapy conducted within a working private practice. The specific questions we are addressing are, whether the requests clients bring to their first session of therapy can be reliably classified by practitioners, whether these requests change over time, and whether the nature of the request is associated with therapist and client ratings of therapeutic outcome. Additional questions about the form and nature of the therapeutic alliance as experienced by both client and practitioner are also being explored. This paper will map the passage of the work from inception to its current state where over 140 clients are active participants. In doing so attention will be paid to the obstacles encountered: practical, financial and ethical, and the solutions devised to address these.     

Structural Nested Models and Standard Software: A Mathematical Foundation through Partial Likelihood

Abstract.  In observational studies treatment may be adapted to the patient's state during the course of time. These covariates may in turn also react on the treatment under study, and so on. This makes it hard to distinguish between treatment effect and selection bias. Structural nested models aim at estimating treatment effect in such complicated situations, even when treatment may change at any time. We show that structural nested models can often be calculated with standard software, by using standard models to predict treatment as a tool to estimate treatment effect. Robins ( Survival analysis, Volume 6 of Encyclopedia of Biostatistics , John Wiley and Sons, Chichester, 1998) conjectured this, but so far it was unproven. We use a partial likelihood approach to choose the estimators and tests as a subclass of the estimators and tests in Lok (math. ST/0410271 at http://arXiv.org , 2004). We show that this is the class of estimators and tests that can be calculated with standard software. The estimators are consistent and asymptotically normal, and have interesting asymptotic properties.     

Youth-Subcultural Studies: Sociological Traditions and Core Concepts

The study of youth subcultures has rich histories in the USA and UK, yet has remained a marginal subfield within cultural sociology. In this article, I begin by reviewing the significance of the Chicago school, strain theory, Birmingham school and post-subcultural studies traditions of youth-cultural and youth-subcultural research. I then conceive of a series of significant analytic concepts that over time have proven themselves to be core components of youth-subcultural studies. These analytic concepts include subcultural style, resistance, subcultural space and media, societal reaction, and identity and authenticity. In each analytic section, I explore major conceptual frames and discuss significant empirical research, on youth subcultures including punk goth, straightedge, riot grrrl skateboarding, rave and club cultures, among others.     

Adoption,personality disorder and parental guilt: Implications of genetic research for social work

Few adoptive parents are aware that serious behavioral and personality problems can occur in adopted children. When such problems are manifested, parents are often guilt-ridden, believing that they are to blame. Recent research suggests that the risks associated with adoption may be genetically linked. Social workers who counsel adoptive parents need to be aware of potential problems, explain possible risks to them, and use these research findings to positive effect.     

Prediction in Some Poisson Errors in Variables Models

Predictive distributions are developed and illustrated for prediction in some Poisson errors in variables models. Two different situations in which multiplicative treatment effects are appropriate are considered within the context of predicting counts of road accidents. Hierarchical prior structures are investigated, and numerical integration and Gibbs sampling routines are used to derive the predictive and posterior probabilities. Examples of analyses are provided with data from road accidents in Sweden.