A Bayesian nonparametric causal model

Typically, in the practice of causal inference from observational studies, a parametric model is assumed for the joint population density of potential outcomes and treatment assignments, and possibly this is accompanied by the assumption of no hidden bias. However, both assumptions are questionable for real data, the accuracy of causal inference is compromised when the data violates either assumption, and the parametric assumption precludes capturing a more general range of density shapes (e.g., heavier tail behavior and possible multi-modalities). We introduce a flexible, Bayesian nonparametric causal model to provide more accurate causal inferences. The model makes use of a stick-breaking prior, which has the flexibility to capture any multi-modalities, skewness and heavier tail behavior in this joint population density, while accounting for hidden bias. We prove the asymptotic consistency of the posterior distribution of the model, and illustrate our causal model through the analysis of small and large observational data sets.     

Transnational diaspora entrepreneurship in emerging markets: Bridging institutional divides

Transnational diasporan entrepreneurs are migrants and their descendants who establish entrepreneurial activities that span the national business environments of their countries of origin and countries of residence. We explore how business incubators contribute to the economic development of emerging markets by organizing their programs to bridge the institutional divides that transnational diaspora entrepreneurs face when establishing their multi-territorial ventures in these markets. Drawing on qualitative fieldwork conducted in the Netherlands during 2007, we present the case study of IntEnt, an incubator providing services exclusively to transnational diasporan entrepreneurs. We apply Bergek and Norrman's (2008) assessment framework to this case, examining IntEnt's goals, model (selection, business support, and mediation activities), and outcomes. Employing Eisenhardt's case-based theory development approach (1989), we then leverage case findings to generate theory about the institutional challenges faced by transnational diaspora entrepreneurs and the role that incubators can play in helping these entrepreneurs overcome these challenges.     

Statistical Process Control and Condition‐Based Maintenance: A Meaningful Relationship through Data Sharing

This paper focuses on the close relationship between statistical process control and preventive maintenance (PM) of manufacturing equipment. The context is very general: a production process that is characterized by multiple distinct operational states and a failure state. The operational states differ in terms of operational/quality costs and/or the proneness to complete failure. The times of shift from the normal operational state to an inferior one and the times to failure are random variables, not necessarily exponentially distributed. The process is monitored with a control chart with the purpose of quickly detecting shifts to an inferior operational state due to the occurrence of some unobservable assignable cause. At the same time, the information collected from the process may be used to re‐schedule the planned PM, if there is evidence that a failure is imminent. The two mechanisms are obviously related, especially if they are based on measurements of the same critical process characteristic. Yet, they are typically treated independently. We develop a fairly general mathematical model for the joint optimization of the control chart parameters and the maintenance times. Numerical investigation using this model shows that ignoring the close relationship between process control and maintenance results in inefficiencies that may be substantial. It also provides practical insights about the effects of some key problem characteristics on the optimal joint design of process control and maintenance.     

Simultaneous confidence bands for nonlinear regression models

The maximization and minimization procedure for constructing confidence bands about general regression models is explained. Then, using an existing confidence region about the parameters of a nonlinear regression model and the maximization and minimization procedure, a generally conservative simultaneous confidence band is constructed about the model. Two examples are given, and some problems with the procedure are discussed     

Social Workers,Intimate Partner Violence (IPV), and Client Financial Concerns

ABSTRACT

The negative impact of financial abuse on intimate partner violence (IPV) survivors is well researched (Babcock, Waltz, Jacobson, & Gottman, 1993 Babcock, J. C., Waltz, J., Jacobson, N. S., & Gottman, J. M. (1993). Power and violence: The relation between communication patterns, power discrepancies, and domestic violence. Journal of Consulting and Clinical Psychology, 61(1), 40–50. doi:10.1037/0022-006X.61.1.40[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), however literature indicating how often social workers ask clients about financial concerns is currently limited. This study examined the frequency with which social workers asked clients about their financial concerns and how that relates to perceived knowledge of IPV and perceived preparedness to work with clients experiencing IPV. A total of 266 social workers who were alumni of a university located on the eastern shore of the United States responded to the questionnaire. Fewer than 30% of respondents reported that they always asked clients about their financial concerns. Logistic regression was used to model the association of always asking clients about financial concerns as a function of perceived knowledge (model 1) and perceived preparedness (model 2). In model 1, perceived knowledge, IPV education and/or training, and age were significant predictors of always asking about financial concerns (p < .05), while in model 2, perceived preparedness and age were significant predictors (p < .05). More research is needed to explore social workers' comfort with addressing client financial concerns and implications for practice.     

Marginal maximum likelihood estimation methods for the tuning parameters of ridge,power ridge,and generalized ridge regression

This study introduces fast marginal maximum likelihood (MML) algorithms for estimating the tuning (shrinkage) parameter(s) of the ridge and power ridge regression models, and an automatic plug-in MML estimator for the generalized ridge regression model, in a Bayesian framework. These methods are applicable to multicollinear or singular covariate design matrices, including matrices where the number of covariates exceeds the sample size. According to analyses of many real and simulated datasets, these MML-based ridge methods tend to compare favorably to other tuning parameter selection methods, in terms of computation speed, prediction accuracy, and ability to detect relevant covariates.     

The effects of backpack load carrying on dynamic balance as measured by limits of stability

PURPOSE: The purpose of this research was to determine if standing dynamic balance was affected by carrying a backpack. SUBJECTS: Data was obtained from 50 healthy college students. MATERIALS AND METHODS: Limits of stability was assessed using the Smart Equitest Balance Master System(R). Reaction time, movement velocity, end point excursion, maximum excursion, and directional control were measured to evaluate movement, with and without a loaded backpack. DATA ANALYSIS: Reliability was established using an Intra-Class Correlation Coefficient (2,1). MANOVA was utilized to analyze the effect of the backpack. SUMMARY DATA: Movement velocity significantly decreased during backpack loaded trials (p=0.004). Directional control was significantly different with respect to direction (p=0.006). No significant difference in reaction time, maximum excursion, or end point excursion was observed with backpack loading (p=0.10-0.93). CONCLUSION: This study concludes that backpack load carrying has an effect on movement velocity and directional control.     

A HEURISTIC TECHNIQUE FOR TESTING FOR THE PRESENCE OF INTERACTION IN NONREPLICATED FACTORIAL EXPERIMENTS

Abstract  This paper suggests univariate and multivariate techniques for investigating interaction in nonreplicated factorial experiments. The tests can detect certain types of interaction, but they are not powerful against all possible alternative hypotheses. The two-way factorial experiment is discussed in some detail and an example is used to demonstrate the procedure. The procedure is compared to other tests for interaction. These comparisons show that the procedure can detect some types of interaction which other tests cannot. Likewise other tests can detect interaction this procedure cannot.     

Tiered Chemical Testing: A Value of Information Approach

In December 2000 the EPA initiated the Voluntary Children's Chemical Evaluation Program (VCCEP) by asking manufacturers to voluntarily sponsor toxicological testing in a tiered process for 23 chemicals selected for the pilot phase. The tiered nature of the VCCEP pilot program creates the need for clearly defined criteria for determining when information is sufficient to assess the potential risks to children. This raises questions about how to determine the "adequacy" of the existing information and assess the need to undertake efforts to reduce uncertainty (through further testing). This article applies a value of information analysis approach to determine adequacy by modeling how toxicological and exposure data collected through the VCCEP may be used to inform risk management decisions. The analysis demonstrates the importance of information about the exposure level and control costs in making decisions regarding further toxicological testing. This article accounts for the cost of delaying control action and identifies the optimal testing strategy for a constrained decisionmaker who, absent applicable human data, cannot regulate without bioassay data on a specific chemical. It also quantifies the differences in optimal testing strategy for three decision criteria: maximizing societal net benefits, ensuring maximum exposure control while net benefits are positive (i.e., benefits outweigh costs), and controlling to the maximum extent technologically feasible while the lifetime risk of cancer exceeds a specific level of risk. Finally, this article shows the large differences that exist in net benefits between the three criteria for the range of exposure levels where the optimal actions differ.