Connectedness in a general model for valued networks

Social networks have been closely identified with graph theoretical models, which constitute their most familiar mode of representation. There are a number of such models which may embody symmetric, directed, or valued relationships. But the study of networks with valued linkages, using the natural formalization provided by the valued graph or digraph, has been impeded by a traditional lack of analytical machinery for dealing with valued structures. In this paper, we demonstrate the development and elaboration of formalizations for the central network concepts of reachability, joining, and connectedness through graph theoretical models of increasing complexity, culminating in their expression within a general model for valued structures. This model for valued (symmetric or directed) graphs, or vigraphs, provides a unified representation and matrix methodology for dealing with qualitative and quantitative structures, incorporates many existing methods as special cases, and suggests new applications. Some of the most interesting of these follow the recognition, consistent with the model, that the “values” assigned to network linkages may be sorts of entities other than numbers.     

A Gompertz fit that fits: Applications to canadian fertility patterns

In this paper an attempt is made to refine the method of fitting the Gompertz function to the cumulative fertility rates by using iterative techniques. The method is tested with the historical data series for the Canadian population. The demographic implication of the parameters of the Gompertz function as fitted to the fertility distribution is examined, and the usefulness of the method in projecting future fertility trends is studied. The Makeham function is also fitted to the fertility distribution by the same iterative technique, and the relative efficiency of this function is compared with that of the Gompertz.     

Identifying Treatment Effects Under Data Combination

We consider the identification of counterfactual distributions and treatment effects when the outcome variables and conditioning covariates are observed in separate data sets. Under the standard selection on observables assumption, the counterfactual distributions and treatment effect parameters are no longer point identified. However, applying the classical monotone rearrangement inequality, we derive sharp bounds on the counterfactual distributions and policy parameters of interest.     

Quantifying Water Pathogen Risk in an Epidemiological Framework

Traditionally, microbial risk assessors have used point estimates to evaluate the probability that an individual will become infected. We developed a quantitative approach that shifts the risk characterization perspective from point estimate to distributional estimate, and from individual to population. To this end, we first designed and implemented a dynamic model that tracks traditional epidemiological variables such as the number of susceptible, infected, diseased, and immune, and environmental variables such as pathogen density. Second, we used a simulation methodology that explicitly acknowledges the uncertainty and variability associated with the data. Specifically, the approach consists of assigning probability distributions to each parameter, sampling from these distributions for Monte Carlo simulations, and using a binary classification to assess the output of each simulation. A case study is presented that explores the uncertainties in assessing the risk of giardiasis when swimming in a recreational impoundment using reclaimed water. Using literature-based information to assign parameters ranges, our analysis demonstrated that the parameter describing the shedding of pathogens by infected swimmers was the factor that contributed most to the uncertainty in risk. The importance of other parameters was dependent on reducing the a priori range of this shedding parameter. By constraining the shedding parameter to its lower subrange, treatment efficiency was the parameter most important in predicting whether a simulation resulted in prevalences above or below non outbreak levels. Whereas parameters associated with human exposure were important when the shedding parameter was constrained to a higher subrange. This Monte Carlo simulation technique identified conditions in which outbreaks and/or nonoutbreaks are likely and identified the parameters that most contributed to the uncertainty associated with a risk prediction.     

Caring: commitment to excellence or condemnation to conformity?

The art of caring to alleviate illness and to promote health is nursing's unique commitment to society and the health-care industry. How this practice is implemented dictates the success or failure of prescribed strategies. In addition, the way in which caring is implemented defines emotional maturity on both a personal and professional level. Several goals to rehabilitate the psychologically codependent person include: Developing an awareness of the origins of codependency and how early family experiences affect subsequent behavior and beliefs about oneself; Identifying the personal price and payoffs for continuing codependent behaviors; The ability to openly express personal needs, wishes, feelings, and opinions while respecting the rights of others; Learning to discriminate between loving/caring and the destructive control of codependency; and The ability to take responsibility for another rather than being responsible to another. Codependent behaviors are prevalent within professional nursing practice, as evidenced by examples provided from three specialty areas in nursing and the fact that women (who are traditionally assigned the cultural role of caring) constitute the majority of professional nurses. Therefore, it is a professional challenge to each nurse to ascertain whether practices are functional or dysfunctional: is the professional interpretation of caring a commitment to excellence or a condemnation to conformity in the unique delivery of health-care practice?     

Dementia Caregiving in the Context of Late‐Life Remarriage: Support Networks,Relationship Quality,and Well‐being

Caregiving research has not accounted for increasingly diverse and complex marital and family histories of older Americans. The authors examined social relations and care‐specific positive and negative support networks among late‐life remarried wife dementia caregivers (N = 61) to determine associations among network structure, relationship quality with and support received from network members, and global assessments of family and stepfamily disagreement on caregiver well‐being. Own family and friends predominated in the social relations and positive networks. Although over half (54%) of respondents included a stepfamily member in their positive networks, stepchildren comprised the largest group (35%) in the negative networks. Larger negative networks and actively negative interactions were related to greater caregiver burden, and more global disagreement with stepfamily was associated with greater depression and burden. The findings illustrate the complex nature of support and the value of targeted examinations of caregiving support dynamics among late‐life remarried older adults and stepfamilies facing health demands.     

Robust inference for bivariate point processes

In the analysis of recurrent events where the primary interest lies in studying covariate effects on the expected number of events occurring over a period of time, it is appealing to base models on the cumulative mean function (CMF) of the processes (Lawless & Nadeau 1995). In many chronic diseases, however, more than one type of event is manifested. Here we develop a robust inference procedure for joint regression models for the CMFs arising from a bivariate point process. Consistent parameter estimates with robust variance estimates are obtained via unbiased estimating functions for the CMFs. In most situations, the covariance structure of the bivariate point processes is difficult to specify correctly, but when it is known, an optimal estimating function for the CMFs can be obtained. As a convenient model for more general settings, we suggest the use of the estimating functions arising from bivariate mixed Poisson processes. Simulation studies demonstrate that the estimators based on this working model are practically unbiased with robust variance estimates. Furthermore, hypothesis tests may be based on the generalized Wald or generalized score tests. Data from a trial of patients with bronchial asthma are analyzed to illustrate the estimation and inference procedures.