University of Windsor

About: University of Windsor is a education organization based out in Windsor, Ontario, Canada. It is known for research contribution in the topics: Population & Argumentation theory. The organization has 10654 authors who have published 22307 publications receiving 435906 citations. The organization is also known as: UWindsor & Assumption University of Windsor.

Predictors of Acculturation for Chinese Adolescents in Canada: Age of Arrival, Length of Stay, Social Class, and English Reading Ability

Abstract: In a sample of 506 Chinese adolescents living in Canada from 3 cohort groups, age at the time of arrival in Canada, length of stay in Canada, socioeconomic status, and English reading ability predicted acculturation. English reading ability and socioeconomic status predicted acculturative stress. There were within-group cohort differences in acculturation characteristics. Implications for counseling are addressed. En una muestra de 506 adolescentes Chinos que viven en el Canada de 3 cohortes, la edad de llegada a Canada, el tiempo que vivieron en Canada, la posicion socioeconomica, y la habilidad de leer Ingles predice la asimilacion. La habilidad de leer Ingles y la posicion socioeconomica que pronostico el estres de asimilacion. Estaban entre grupos de cohorte diferentes en caracteristicas de asimilacion. Las implicaciones para terapia se dirigen.

Invader Relative Impact Potential: a new metric to understand and predict the ecological impacts of existing, emerging and future invasive alien species

Abstract: Summary Predictions of the identities and ecological impacts of invasive alien species are critical for risk assessment, but presently we lack universal and standardized metrics that reliably predict the likelihood and degree of impact of such invaders (i.e. measurable changes in populations of affected species). This need is especially pressing for emerging and potential future invaders that have no invasion history. Such a metric would also ideally apply across diverse taxonomic and trophic groups. We derive a new metric of invader ecological impact that blends: (i) the classic Functional Response (FR; consumer per capita effect) and Numerical Response (NR; consumer population response) approaches to determining consumer impact, that is, the Total Response (TR = FR × NR), with; (ii) the ‘Parker–Lonsdale equation’ for invader impact, where Impact = Range × Abundance × Effect (per capita effect), into; (iii) a new metric, Relative Impact Potential (RIP), where RIP = FR × Abundance. The RIP metric is an invader/native ratio, where values >1 predict that invader ecological impact will occur, and increasing values above 1 indicate increasing impact. In addition, the invader/invader RIP ratio allows comparisons of the ecological impacts of different invaders. Across a diverse range of trophic and taxonomic groups, including predators, herbivores, animals and plants (22 invader/native systems with 47 individual comparisons), high-impact invaders were significantly associated with higher FRs compared to native trophic analogues. However, the RIP metric substantially improves this association, with 100% predictive power of high-impact invaders. Further, RIP scores were significantly and positively correlated with two independent ecological impact scores for invaders, allowing prediction of the degree of impact of invasive alien species with the RIP metric. Finally, invader/invader RIP scores were also successful in identifying and associating with higher impacting invasive alien species. Synthesis and applications. The Relative Impact Potential metric combines the per capita effects of invaders with their abundances, relative to trophically analogous natives, and is successful in predicting the likelihood and degree of ecological impact caused by invasive alien species. As the metric constitutes readily measurable features of individuals, populations and species across abiotic and biotic context-dependencies, even emerging and potential future invasive alien species can be assessed. The Relative Impact Potential metric can be rapidly utilized by scientists and practitioners and could inform policy and management of invasive alien species across diverse taxonomic and trophic groups.

Simplifying the Implication-Realization Model of Melodic Expectancy

Abstract: Results from previous investigations indicate that the implication-realization (I-R) model (Narmour, 1990) of expectancy in melody may be overspecified and more complex than necessary. Indeed, Schellenberg9s (1996) revised model, with two fewer predictor variables, improved predictive accuracy compared with the original model. A reanalysis of data reported by Cuddy and Lunney (1995) provided similar results. When the principles of the I-R model were submitted to a principal- components analysis, a solution containing three orthogonal (uncorrelated) factors retained the accuracy of the model but was inferior to the revised model. A separate principal-components analysis of the predictors of the revised model yielded a two-factor solution that did not compromise the revised model9s predictive power. Consequently, an even simpler model of melodic expectancy was derived. These results provide further evidence that redundancy in the I-R model can be eliminated without loss of predictive accuracy.

Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging

Abstract: We study the effectiveness of cross validation, Bayesian regularization, early stopping, and bagging to mitigate overfitting and improving generalization for pricing and hedging derivative securities with daily S&P 500 index daily call options from January 1988 to December 1993. Our results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors than the baseline neural-network (NN) model and the Black-Scholes model for some years. While early stopping does not affect the pricing errors, it significantly reduces the hedging error (HE) in four of the six years we investigated. Although computationally most demanding, bagging seems to provide the most accurate pricing and delta hedging. Furthermore, the standard deviation of the MSPE of bagging is far less than that of the baseline model in all six years, and the standard deviation of the average HE of bagging is far less than that of the baseline model in five out of six years. We conclude that they be used at least in cases when no appropriate hints are available.