Iterative algebras

Given a finitely generated free monoid $X$ and a morphism $\phi : X\to X$, we show that one can construct an algebra, which we call an iterative algebra, in a natural way. We show that many ring theoretic properties of iterative algebras can be easily characterized in terms of linear algebra and combinatorial data from the morphism and that, moreover, it is decidable whether or not an iterative algebra has these properties. Finally, we use our construction to answer several questions of Greenfeld, Leroy, Smoktunowicz, and Ziembowski by constructing a primitive graded nilpotent algebra with Gelfand-Kirillov dimension two that is finitely generated as a Lie algebra.Comment: 13 page

Screening For Trauma In the Foster Care Community

Children within the foster care community experience health disparities on a variety of levels, from physical and mental health conditions, developmental delay, and impaired social interactions. Many of these conditions can stem from the experience of trauma or adverse childhood events that color each component of their lives. For healthcare providers to be successful in the assessment and treatment of this community, an understanding of traumatic events and the delivery of trauma-informed care is essential. The complex needs of this community are such that the provision of healthcare must be specialized, multidimensional, and organized. The purpose of this project is to assist in the development of a clinic that specializes in the delivery of primary care to the foster care community, with a focus on screening and assessment for the experience of trauma. Children were screened for child trauma as well as for resiliency behaviors, and a tool for chart review was used to examine current health status and services. With the assistance of stakeholders, meticulous follow up on screening results, referrals and recommendations made ensured that no opportunities were missed to provide the highest quality care for this underserved community

Testing for Neglected Nonlinearity in Cointegrating Relationships

This paper proposes pure significance tests for the absence of nonlinearity in cointegrating relationships. No assumption of the functional form of the nonlinearity is made. It is envisaged that the application of such tests could form the first step towards specifying a nonlinear cointegrating relationship for empirical modelling. The asymptotic and small sample properties of our tests are investigated, where special attention is paid to the role of nuisance parameters and a potential resolution using the bootstrap.Cointegration, Nonlinearity, Neural networks, Bootstrap

Testing for ARCH in the Presence of Nonlinearity of Unknown Form in the Conditional Mean

Tests of ARCH are a routine diagnostic in empirical econometric and financial analysis. However, it is well known that misspecification of the conditional mean may lead to spurious rejections of the null hypothesis of no ARCH. Nonlinearity is a prime example of this phenomenon. There is little work on the extent of the effect of neglected nonlinearity on the properties of ARCH tests. This paper provides some such evidence and also new ARCH testing procedures that are robust to the presence of neglected nonlinearity. Monte Carlo evidence shows that the problem is serious and that the new methods alleviate this problem to a very large extent.Nonlinearity, ARCH, Neural networks

Detecting superior mutual fund managers: evidence from copycats

We examine the ex ante ability of investors to identify superior mutual fund managers among the investor set likely most able, and with the greatest incentive to do so, their rivals. Identifying actual copycat funds via comparisons of trading in consecutive periods, we find little evidence to suggest that managers are able to detect superior funds. Copycats select funds with high prior performance and investment inflows, and the performance of the target fund reverses following copying initiation. If superior managers exist, our results suggest that the source of skill lies in private information obtained by these managers. These results are consistent with information models indicating that private, but not public, information can be profitabl

Boosting Estimation of RBF Neural Networks for Dependent Data

This paper develops theoretical results for the estimation of radial basis function neural network specifications, for dependent data, that do not require iterative estimation techniques. Use of the properties of regression based boosting algorithms is made. Both consistency and rate results are derived. An application to nonparametric specification testing illustrates the usefulness of the results.Neural Networks, Boosting

The identification of gamma ray induced EAS

Some of the penetrating particles in gamma-induced EAS from Cygnus X-3 observed by a single layer of flash-bulbs under 880 g cm/2 concrete, may be punched through photons rather than muons. An analysis of the shielded flash-tube response detected from EAS is presented. The penetration of the electro-magnetic component through 20 cm of Pb is observed at core distances approx. 10 m