Sampling Strategies for Toxic Air Contaminants

Annual concentrations of toxic air contaminants are of primary concern from the perspective of chronic human exposure assessment and risk analysis. Despite recent advances in air quality monitoring technology, resource and technical constraints often impose limitations on the availability of a sufficient number of ambient concentration measurements for performing environmental risk analysis. Therefore, sample size limitations, representativeness of data, and uncertainties in the estimated annual mean concentration must be examined before performing quantitative risk analysis. In this paper, we discuss several factors that need to be considered in designing field-sampling programs for toxic air contaminants and in verifying compliance with environmental regulations. Specifically, we examine the behavior of SO2, TSP, and CO data as surrogates for toxic air contaminants and as examples of point source, area source, and line source-dominated pollutants, respectively, from the standpoint of sampling design. We demonstrate the use of bootstrap resampling method and normal theory in estimating the annual mean concentration and its 95% confidence bounds from limited sampling data, and illustrate the application of operating characteristic (OC) curves to determine optimum sample size and other sampling strategies. We also outline a statistical procedure, based on a one-sided t-test, that utilizes the sampled concentration data for evaluating whether a sampling site is compliance with relevant ambient guideline concentrations for toxic air contaminants.     

Government Risk Management Priorities: A Comparison of the Preferences of Asian Indian Americans and Other Americans

A survey was conducted of approximately 200 Asian Indian Americans and 200 other residents of New Jersey in order to understand the risk management priorities that they want government to have. We found that Asian Indian Americans, especially younger women, focused on personal/family risks, such as alcohol and drug abuse, sexual abuse, and domestic violence. The New Jersey comparison group, in contrast, placed war/terrorism and loss of health care services and insurance at the top of their priorities for government. These results suggest stressful acculturation-related issues within the Asian Indian community. Both populations want more risk management from government than they believe government is currently providing. Respondents who wanted more from government tended to dread the risk, be fearful of the consequences, trust government, and have a feeling of personal efficacy. Within the Asian Indian American sample, wide variations were observed by language spoken at home and religious affiliation. Notably, Muslims and Hindi language speakers tended not to trust government and hence wanted less government involvement. This study supports our call for studies of recent migrant populations and Johnson's for testing ethnic identity and acculturation as factors in risk judgments.     

Grounded Neural Networking: Modeling Complex Quantitative Data

The latest advances in artificial intelligence software (neural networking) have finally made it possible for qualitative researchers to apply the grounded theory method to the study of complex quantitative databases in a manner consistent with the postpositivistic, neopragmatic assumptions of most symbolic interactionists. The strength of neural networking for the study of quantitative data is twofold: it blurs the boundaries between qualitative and quantitative analysis, and it allows grounded theorists to embrace the complexity of quantitative data. The specific technique most useful to grounded theory is the Self‐Organizing Map (SOM). To demonstrate the utility of the SOM we (1) provide a brief review of grounded theory, focusing on how it was originally intended as a comparative method applicable to both quantitative and qualitative data; (2) examine how the SOM is compatible with the traditional techniques of grounded theory; and (3) demonstrate how the SOM assists grounded theory by applying it to an example based on our research.     

Local polynomial estimation of Poisson intensities in the presence of reporting delays

Summary.  The system for monitoring suicides in Hong Kong has considerable delays in reporting as the cause of death needs to be determined by a coroner's investigation. However, timely estimates of suicide rates are desirable to assist in the formulation of public health policies. This motivated us to develop a non-parametric procedure to estimate the intensity function of a Poisson process in the presence of reporting delays. We give closed form estimators of the Poisson intensity and the delay distribution, conduct simulation studies to evaluate the method proposed and derive their asymptotic properties. The method proposed is applied to estimate the intensity of suicide in Hong Kong.     

Bayesian analysis of directed graphs data with applications to social networks

Summary.  A fully Bayesian analysis of directed graphs, with particular emphasis on applica- tions in social networks, is explored. The model is capable of incorporating the effects of covariates, within and between block ties and multiple responses. Inference is straightforward by using software that is based on Markov chain Monte Carlo methods. Examples are provided which highlight the variety of data sets that can be entertained and the ease with which they can be analysed.     

Stripping in the New Millennium: Thinking about Trends in Exotic Dance and Dancers' Lives

With the increasing presence of sex and sexuality in American popular culture, the stripping industry is undoubtedly undergoing important subcultural and organizational transition. What are the implications of these changes for the individuals involved? The current paper reviews much of the literature on exotic dance to consider the potential transformations in the organization of the sex industry and how these changes may impact the lives of those involved. Specifically, I note some key differences in the literature that suggest how exotic dance has shifted. I then pose some questions for empirical inquiry. I argue that transformations in the exotic industry are creating new opportunities and challenges for women, and may be particularly deleterious for poor and minority women. I call for sociologists to investigate the consequences of organizational transferal in sexual labor, reevaluate the dynamics of agency and exploitation, and consider the potential differential impact of these trends on poor women and women of color.     

A regression-based smoothing spline Monte Carlo algorithm for pricing American options in discrete time

American options in discrete time can be priced by solving optimal stopping problems. This can be done by computing so-called continuation values, which we represent as regression functions defined recursively by using the continuation values of the next time step. We use Monte Carlo to generate data, and then we apply smoothing spline regression estimates to estimate the continuation values from these data. All parameters of the estimate are chosen data dependent. We present results concerning consistency and the estimates’ rate of convergence.     

Classifying At‐Risk High School Youth: The Influence of Exposure to Community Violence and Protective Factors on Academic and Health Outcomes

Using cluster analysis, 789 predominately Latino and African American high school youth were classified into varying academic at‐risk profiles using self‐reported levels of academic confidence, motivation to attend school, perceived family support, connections with teachers and peers, and exposure to violence. Six clusters emerged, 5 of which were identified as “at‐risk.” The clusters were examined in relation to academic stress, health status, grades, and school retention. Exposure to violence was one distinguishing feature of youth identified as most vulnerable, vulnerable, and resilient; however, youth identified as resilient recorded better academic outcomes.