THE EFFECT OF MATERNAL DRUG USE ON BIRTH WEIGHT: MEASUREMENT ERROR IN BINARY VARIABLES

We develop a method to correct for non-random measurement error in a binary indicator of illicit drugs. Our results suggest that estimates of the effect of self-reported prenatal drug use on birth weight are biased upwards by measurement error—a finding contrary to predictions of a model of random measurement error. More accurate estimates of the true effect of drug use on birth weight can be obtained by using the predicted probability of falsely reporting drug use. Thus out-of-sample information on drug use may improve estimates of the effect of reported drug use in other settings.     

Virtual Governance Networking Policies: A Comparative Analysis

Networks have become an increasingly important mode of public governance. ICT mechanisms, especially virtual systems will potentially establish this idea. This paper is based on a comparative analysis of three cases which are chosen to be particularly illustrative of the three virtual governance networking policies. In light of case study results, we have demonstrated that it is possible to devise a theoretically acceptable prospect of world countries concerning virtual governance networking policies.     

An INAR(1) model based on a mixed dependent and independent counting series

A mixed integer-valued autoregressive model of order one, based on the binomial and the generalized binomial thinning operator is introduced. Geometric marginal distribution is considered. Properties of the model are analysed, unknown parameters are estimated and some numerical results of the estimates are obtained. Finally, model is applied on two real data sets and compared to some relevant models.     

Loss volatility,bankruptcy, and the demand for reinsurance

Insurers in our model reinsure to lower the risk of bankruptcy. In the conceptual part of the study, we show that given bankruptcy cost, reinsurance may be demanded even if the insurer is risk-neutral. The model allows us to assess how the insurer's surplus, size, and volatility of losses affect the amount of reinsurance the insurer purchases. As predicted by our comparative statics analysis, we find empirically that property/casualty and medical malpractice insurers with higher prereinsurance loss volatility, lower surplus-to-premium ratios, and smaller sizes demand more reinsurance.     

Residential choices of immigrants in Australia

"In recent years Asian-Australians, especially those born in Indochina, have become a target of criticism by anti-immigration groups which accuse them of choosing to live only in ?closed societies' and ?ethnic ghettos' in Australian cities.... Evidence presented in the article shows clearly that while immigrant groups may prefer to settle in close proximity of their family and kin for practical and/or emotional reasons, their preference is not ethnically determined.... We also show that affordability, closeness to spouse's work, and neighbourhood services, rather than family and social contacts, are important factors for Asian-born immigrants when they move." (SUMMARY IN FRE AND SPA)     

Leveraging the Strengths of Choice Models and Neural Networks: A Multiproduct Comparative Analysis*

Choice models and neural networks are two approaches used in modeling selection decisions. Defining model performance as the out‐of‐sample prediction power of a model, we test two hypotheses: (i) choice models and neural network models are equal in performance, and (ii) hybrid models consisting of a combination of choice and neural network models perform better than each stand‐alone model. We perform statistical tests for two classes of linear and nonlinear hybrid models and compute the empirical integrated rank (EIR) indices to compare the overall performances of the models. We test the above hypotheses by using data for various brand and store choices for three consumer products. Extensive jackknifing and out‐of‐sample tests for four different model specifications are applied for increasing the external validity of the results. Our results show that using neural networks has a higher probability of resulting in a better performance. Our findings also indicate that hybrid models outperform stand‐alone models, in that using hybrid models guarantee overall results equal or better than the two stand‐alone models. The improvement is particularly significant in cases where neither of the two stand‐alone models is very accurate in prediction, indicating that the proposed hybrid models may capture aspects of predictive accuracy that neither stand‐alone model is capable of on their own. Our results are particularly important in brand management and customer relationship management, indicating that multiple technologies and mixture of technologies may yield more accurate and reliable outcomes than individual ones.     

A new class of skew distributions with climate data analysis

In this paper, we develop a new general class of skew distributions with flexibility properties on the tails. Moreover, such class can provide heavy and light tails. Some of its mathematical properties are studied, including the quantile function, the moments, the moment generating function and the mean of deviations. New skew distributions are derived and used to construct new models capturing asymmetry inherent to data. The estimation of the class parameters is investigated by the method of maximum likelihood and the performance of the estimators is assessed by a simulation study. Applications of the proposed distribution are explored for two climate data sets. The first data set concerns the annual heat wave index and the second data set involves temperature and precipitation measures from the meteorological station located at Schiphol, Netherlands. Data fitting results show that our models perform better than the competitors.     

Family Focused Care for Refugees and Displaced Populations: Global Opportunities for Family Therapists

Recent global crises have created a significant increase in the number of people leaving their countries. Distress experienced by these refugees often leads to posttraumatic stress disorder and depression and can also result in psychotic disorders, substance abuse, and interpersonal violence. The World Health Organization leads the organizing of refugee services as part of a larger initiative to provide mental health services to citizens in low‐ and middle‐income countries. The World Health Organization has identified challenges in providing care, including a provider shortage, issues with how refugees access and receive care and a lack of uniformity in mental health services. By applying the values and systemic orientation of the profession, family therapists can address some of the challenges in treating mental health concerns of these at‐risk populations.