Behavioral responses of rural and urban greater white-toothed shrews (Crocidura russula) to sound disturbance

Urban Ecosystems - The development of urban areas imposes challenges that wildlife must adapt to in order to persist in these new habitats. One of the greatest changes brought by urbanization has...     

Review Essay: Constructing the Sociology of Social Problems: Spector and Kitsuse Twenty-Five Years Later

Sociological Forum -     

A solution of the externality problem using strategic matching

This paper develops a Coase-like solution of the problem of inducing Pareto optimal behavior in the presence of reciprocal externalities. In place of Coasean direct compensation between the parties to an externality problem, actors strategically match each other's externality-producing activity, and thus induce counterparts to internalize the external benefits or costs of their actions. The analysis suggests a general framework for analyzing social interactions in the presence of reciprocal externalities. As an application of the theory, a solution of the duopoly problem is noted.We are indebted to L. Danziger, D. Samet, participants in faculty seminars at Bar-Ilan and New York Universities and at the University of Maryland, and two anonymous referees for helpful comments and discussions. All remaining errors are our own.     

Nonparametric smoothing in the analysis of air pollution and respiratory illness

While most of epidemiology is observational, rather than experimental, the culture of epidemiology is still derived from agricultural experiments, rather than other observational fields, such as astronomy or economics. The mismatch is made greater as focus has turned to continue risk factors, multifactorial outcomes, and outcomes with large variation unexplainable by available risk factors. The analysis of such data is often viewed as hypothesis testing with statistical control replacing randomization. However, such approaches often test restricted forms of the hypothesis being investigated, such as the hypothesis of a linear association, when there is no prior empirical or theoretical reason to believe that if an association exists, it is linear. In combination with the large nonstochastic sources of error in such observational studies, this suggests the more flexible alternative of exploring the association. Conclusions on the possible causal nature of any discovered association will rest on the coherence and consistency of multiple studies. Nonparametric smoothing in general, and generalized additive models in particular, represent an attractive approach to such problems. This is illustrated using data examining the relationship between particulate air pollution and daily mortality in Birmingham, Alabama; between particulate air pollution, ozone, and SO₂ and daily hospital admissions for respiratory illness in Philadelphia; and between ozone and particulate air pollution and coughing episodes in children in six eastern U.S. cities. The results indicate that airborne particles and ozone are associated with adverse health outcomes at very low concentrations, and that there are likely no thresholds for these relationships.     

Why Did House Members Vote for H.R. 4437?1

This article examines why members of the U.S. House of Representatives voted for H.R. 4437, the controversial 2005 bill to construct a 700‐mile immigration barrier along the U.S.‐Mexican border and to criminalize illegal presence and aid to undocumented immigrants. Logit analysis suggests that being a first‐term House member or a Republican and representing a district that was in the South or the West or heavily blue‐collar substantially boosted the odds of supporting H.R. 4437. If a member's district was disproportionately Asian, Latino, or, especially, African American, he or she was instead more likely to oppose the measure.     

Machine learning and design of experiments with an application to product innovation in the chemical industry

Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. To this end, a background on the joint application of Design of Experiments (DOE) and Machine Learning (ML) methodologies in industrial settings is presented here, along with a case study from the chemical industry. A DOE study is used to collect data, and two ML models are applied to predict responses which performance show an advantage over the traditional modeling approach. Emphasis is placed on causal investigation and quantification of prediction uncertainty, as these are crucial for an assessment of the goodness and robustness of the models developed. Within the scope of the case study, the models learned can be implemented in a semi-automatic system that can assist practitioners who are inexperienced in data analysis in the process of new product development.     

Estimation of Nonparametric Models With Simultaneity

We introduce methods for estimating nonparametric, nonadditive models with simultaneity. The methods are developed by directly connecting the elements of the structural system to be estimated with features of the density of the observable variables, such as ratios of derivatives or averages of products of derivatives of this density. The estimators are therefore easily computed functionals of a nonparametric estimator of the density of the observable variables. We consider in detail a model where to each structural equation there corresponds an exclusive regressor and a model with one equation of interest and one instrument that is included in a second equation. For both models, we provide new characterizations of observational equivalence on a set, in terms of the density of the observable variables and derivatives of the structural functions. Based on those characterizations, we develop two estimation methods. In the first method, the estimators of the structural derivatives are calculated by a simple matrix inversion and matrix multiplication, analogous to a standard least squares estimator, but with the elements of the matrices being averages of products of derivatives of nonparametric density estimators. In the second method, the estimators of the structural derivatives are calculated in two steps. In a first step, values of the instrument are found at which the density of the observable variables satisfies some properties. In the second step, the estimators are calculated directly from the values of derivatives of the density of the observable variables evaluated at the found values of the instrument. We show that both pointwise estimators are consistent and asymptotically normal.     

Dopamine Genes and Pathological Gambling in Discordant Sib-Pairs

Pathological gambling (PG) is an impulse control disorder that has been considered as a behavioral addiction. Recent studies have suggested the involvement of the dopaminergic system in addictions and impulse control disorders and associations of dopamine receptor genes (DRD1, DRD2, and DRD4) and PG have been reported. In the present study, 140 sib-pairs discordant for the diagnosis of PG (70 males and 70 females on each group) were recruited through the Gambling Outpatient Unit at the Institute of Psychiatry, University of Sao Paulo and were assessed by trained psychiatrists. A family-based association design was chosen to prevent population stratification. All subjects were genotyped for dopamine receptor genes (DRD1 -800 T/C, DRD2 TaqIA RFLP, DRD3 Ser9Gly, DRD4 48bp exon III VNTR, DRD5 (CA) repeat) and the dopamine transporter gene (SCL6A3 40 bp VNTR). Our results suggest the association of PG with DRD1 -800 T/C allele T (P = .03).     

Point subtraction aggression paradigm: validity of a brief schedule of use

The Point Subtraction Aggression Paradigm (PSAP) is a validated behavioral measure of aggression in response to provocation. Canonically, multiple sessions continue until performance asymptotes, requiring significant time. We sought to examine whether use of the PSAP-first-session ("PSAP-FS") had acceptable construct validity for group data, potentially expanding the settings in which the PSAP may be useful. One hundred male and female young adult subjects completed the PSAP-FS and additional behavioral measures of aggression, and provided information related to risk factors. The PSAP-FS correlated significantly with known risk factors of aggression. The PSAP-FS, which measures aggression in the present, correlated well with measures of recent aggression and less strongly with life history of aggression. The PSAP-FS had satisfactory validity properties for use where group-level data are of interest.