Generalized linear models with functional predictors

Summary. We present a technique for extending generalized linear models to the situation where some of the predictor variables are observations from a curve or function. The technique is particularly useful when only fragments of each curve have been observed. We demonstrate, on both simulated and real data sets, how this approach can be used to perform linear, logistic and censored regression with functional predictors. In addition, we show how functional principal components can be used to gain insight into the relationship between the response and functional predictors. Finally, we extend the methodology to apply generalized linear models and principal components to standard missing data problems.     

The use of resighting data to estimate the rate of population growth of the snail kite in Florida

The rate of population growth ( u ) is an important demographic parameter used to assess the viability of a population and to develop management and conservation agendas. We examined the use of resighting data to estimate u for the snail kite population in Florida from 1997-2000. The analyses consisted of (1) a robust design approach that derives an estimate of u from estimates of population size and (2) the Pradel (1996) temporal symmetry (TSM) approach that directly estimates u using an open-population capture-recapture model. Besides resighting data, both approaches required information on the number of unmarked individuals that were sighted during the sampling periods. The point estimates of u differed between the robust design and TSM approaches, but the 95% confidence intervals overlapped substantially. We believe the differences may be the result of sparse data and do not indicate the inappropriateness of either modelling technique. We focused on the results of the robust design because this approach provided estimates for all study years. Variation among these estimates was smaller than levels of variation among ad hoc estimates based on previously reported index statistics. We recommend that u of snail kites be estimated using capture-resighting methods rather than ad hoc counts.     

Bayes model averaging with selection of regressors

Summary. When a number of distinct models contend for use in prediction, the choice of a single model can offer rather unstable predictions. In regression, stochastic search variable selection with Bayesian model averaging offers a cure for this robustness issue but at the expense of requiring very many predictors. Here we look at Bayes model averaging incorporating variable selection for prediction. This offers similar mean-square errors of prediction but with a vastly reduced predictor space. This can greatly aid the interpretation of the model. It also reduces the cost if measured variables have costs. The development here uses decision theory in the context of the multivariate general linear model. In passing, this reduced predictor space Bayes model averaging is contrasted with single-model approximations. A fast algorithm for updating regressions in the Markov chain Monte Carlo searches for posterior inference is developed, allowing many more variables than observations to be contemplated. We discuss the merits of absolute rather than proportionate shrinkage in regression, especially when there are more variables than observations. The methodology is illustrated on a set of spectroscopic data used for measuring the amounts of different sugars in an aqueous solution.     

Making Sense of New Orleans Flood Trauma Recovery: Ethics, Research Design, and Policy Considerations for Future Disasters

This article details the justification and plans of a research team for studying and helping evacuees of Hurricane Katrina from New Orleans to recover by encouraging experimental participants to disclose information about their related experiences in guided interview sessions. Team members' consideration and practice of ethical principles as researchers dealing with the potentially traumatized victims of a disaster are portrayed. Finally, related recommendations for researchers and policy makers are offered.     

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.     

MAXIMUM LIKELIHOOD ESTIMATION FOR A POISSON RATE PARAMETER WITH MISCLASSIFIED COUNTS

This paper proposes a Poisson‐based model that uses both error‐free data and error‐prone data subject to misclassification in the form of false‐negative and false‐positive counts. It derives maximum likelihood estimators (MLEs) for the Poisson rate parameter and the two misclassification parameters — the false‐negative parameter and the false‐positive parameter. It also derives expressions for the information matrix and the asymptotic variances of the MLE for the rate parameter, the MLE for the false‐positive parameter, and the MLE for the false‐negative parameter. Using these expressions the paper analyses the value of the fallible data. It studies characteristics of the new double‐sampling rate estimator via a simulation experiment and applies the new MLE estimators and confidence intervals to a real dataset.     

A Risk-Based Approach to Setting Priorities in Protecting Bridges Against Terrorist Attacks

This article presents an approach to the problem of terrorism risk assessment and management by adapting the framework of the risk filtering, ranking, and management method. The assessment is conducted at two levels: (1) the system level, and (2) the asset-specific level. The system-level risk assessment attempts to identify and prioritize critical infrastructures from an inventory of system assets. The definition of critical infrastructures offered by Presidential Decision Directive 63 was used to determine the set of attributes to identify critical assets--categorized according to national, regional, and local impact. An example application is demonstrated using information from the Federal Highway Administration National Bridge Inventory for the State of Virginia. Conversely, the asset-specific risk assessment performs an in-depth analysis of the threats and vulnerabilities of a specific critical infrastructure. An illustration is presented to offer some insights in risk scenario identification and prioritization, multiobjective evaluation of management options, and extreme-event analysis for critical infrastructure protection.     

The Association of Survey Setting and Mode with Self-Reported Health Risk Behaviors among High School Students

This study examined whether the prevalence of self-reportedhealth risk behaviors among high school students varied by surveysetting (school versus home) and mode of administration (paperand pencil versus computer). Students in grades 9 and 11 wereassigned randomly to one of four conditions—school paper-and-pencilinstrument (PAPI), school computer-assisted self-interview (CASI),home PAPI, and home CASI. During the spring of 2004, 4,506 studentscompleted identically worded questionnaires based on the YouthRisk Behavior Survey questionnaire. Logistic regression analysescontrolling for sex, grade, and race/ethnicity revealed thatsetting was associated significantly with the reporting of 30of the 55 risk behaviors examined, and mode was associated significantlywith the reporting of 7 of the 55 behaviors. For all behaviorswith a significant setting main effect, the odds of reportingthe behavior were greater among students who completed questionnairesat school than among students who completed questionnaires athome. For all behaviors with a significant mode main effect,PAPI mode students had lower odds of reporting the behaviorthan CASI mode students. Because social measurement researchassumes that higher prevalence estimates are more valid thanlower estimates, methodological factors shown to increase estimates,such as setting and mode, should be considered when planningsurveys.     

Book Review: How Emotions Work. By Katz,Jack, Chicago: University of Chicago Press, 1999. 407 pages,hardcover, ISBN: 0226425991

Qualitative Sociology -