A class of rank tests for interaction in two-way layouts

A class of aligned rank order tests for interaction in two-way layouts is considered. The technique is based on application of the existing nonparametric techniques for one-way layouts to the transformed observations. It is shown that the asymptotic properties of these techniques are preserved after alignment. Furthermore, asymptotic relative efficiency comparisons are made with some of the competing tests. Finally, we demonstrate that our technique can be used to test for higher-order interactions in general multi-factor layouts.     

On approximating distribution of the quadratic discriminant function

The quadratic discriminant function (QDF) with known parameters has been represented in terms of a weighted sum of independent noncentral chi-square variables. To approximate the density function of the QDF as m-dimensional exponential family, its moments in each order have been calculated. This is done using the recursive formula for the moments via the Stein's identity in the exponential family. We validate the performance of our method using simulation study and compare with other methods in the literature based on the real data. The finding results reveal better estimation of misclassification probabilities, and less computation time with our method.     

Practices of active citizenship among migrant youth: beyond conventionalities

This paper discusses the empirical manifestations of the notion of active citizenship in the context of the experiences of migrant youth. It focuses on the practices of active citizenship through involvement in social networks and creative civic engagement. In doing so, the article examines the complex and multi-faceted nature of social networking among migrant youth and the extent to which their approach to engagement is dependent on the specificities of the local environment, the type of social issues involved, and the cultural norms of one's own cultural heritage. Key empirical insights are derived from quantitative and qualitative research conducted among migrant youth of African, Arabic-speaking and Pacific Island backgrounds in Australia. These empirical insights are used to examine the changing perceptions of active citizenship among migrant youth, and the possibilities offered through non-traditional networks to engender civic engagement and social participation.     

A dynamic ERP critical failure factors modelling with FCM throughout project lifecycle phases

Implementation of enterprise resource planning systems (ERPs) is a complex and costly task which usually results in serious failures. Numerous factors affect these projects implementation due to their size, complexity and high chance of failure. Therefore, identifying these factors in ERP projects is a critical issue. The majority of previous publications have been conducted in identifying ERP critical success factors (CSFs) rather than critical failure factors (CFFs). In order to help practitioners, this article studies the CFFs in ERP implementation projects. The implications of interdependency among failure factors are also usually overlooked by project managers due to perceived complexity in modelling and analysing influential factors. With this in mind, we have proposed a fuzzy cognitive map (FCM)-based dynamic model of ERP failure factors through project lifecycle phases. The main advantage of FCM lies in them being capable of modelling complex phenomena based on the experts’ perceptions. This tool models uncertainty and related events, imitating human reasoning. Moreover, FCMs enable the developing of forecasting exercises through simulations. Practitioners would thus assess the joint influence of ERP implementation failure factors on project outcomes. The results make known to practitioners which problems will arise if the failure factors are not treated, and how these will impact on the outcomes of projects. Therefore, the proposed approach would help them to manage ERP implementation projects in a more effective and proactive way.     

Functional data: local linear estimation of the conditional density and its application

In this paper, we introduce a new nonparametric estimation procedure of the conditional density of a scalar response variable given a random variable taking values in a semi-metric space. Under some general conditions, we establish both the pointwise and the uniform almost-complete consistencies with convergence rates of the conditional density estimator related to this estimation procedure. Moreover, we give some particular cases of our results which can also be considered as novel in the finite-dimensional setting. Notice also that the results of this paper are used to derive some asymptotic properties of the local linear estimator of the conditional mode.     

On simultaneous confidence intervals based on rank-estimates with application to analysis of gene expression data

Abstract

Inferential methods based on ranks present robust and powerful alternative methodology for testing and estimation. In this article, two objectives are followed. First, develop a general method of simultaneous confidence intervals based on the rank estimates of the parameters of a general linear model and derive the asymptotic distribution of the pivotal quantity. Second, extend the method to high dimensional data such as gene expression data for which the usual large sample approximation does not apply. It is common in practice to use the asymptotic distribution to make inference for small samples. The empirical investigation in this article shows that for methods based on the rank-estimates, this approach does not produce a viable inference and should be avoided. A method based on the bootstrap is outlined and it is shown to provide a reliable and accurate method of constructing simultaneous confidence intervals based on rank estimates. In particular it is shown that commonly applied methods of normal or t-approximation are not satisfactory, particularly for large-scale inferences. Methods based on ranks are uniquely suitable for analysis of microarray gene expression data since they often involve large scale inferences based on small samples containing a large number of outliers and violate the assumption of normality. A real microarray data is analyzed using the rank-estimate simultaneous confidence intervals. Viability of the proposed method is assessed through a Monte Carlo simulation study under varied assumptions.     

Workplace discrimination and diabetes: the EEOC Americans with Disabilities Act research project

Using the Integrated Mission System of the Equal Employment Opportunity Commission, the employment discrimination experience of Americans with diabetes is documented. Researchers compare and contrast the key dimensions of workplace discrimination involving Americans with diabetes and persons with other physical, sensory, and neurological impairments. Specifically, the researchers examine demographic characteristics of the charging parties; the industry designation, location, and size of employers against whom complaints are filed; the nature of discrimination (i.e., type of adverse action) alleged to occur; and the legal outcome or resolution of these complaints. Findings indicate that persons with diabetes were more likely to encounter discrimination involving discharge, constructive discharge, discipline and suspension - all job retention issues. Persons with diabetes were less likely to encounter discrimination involving hiring, reasonable accommodation, non-pension benefits, and layoff. They were also more likely to encounter discrimination when they were older or from specific ethnic backgrounds, or when they worked for small employers or in the Southern United States. Implications for policy and advocacy are addressed.     

Discrimination Measures for Fractional Integrated Models Based on Wavelets

Discrimination measures have been well developed for stationary time series. However in a large number of phenomena, long-term dependencies are involved. In this article, we are dealing with discrimination of fractional integrated models. Kullback–Leibler and Chernoff's discrimination measures are approximated, using the discrete wavelet transform (DWT) for discrimination of these time series classes. The simulation study indicates low misclassification rate, related to the approximations of Kullback–Leibler and Chernoff discrimination measures. Application to problem of classifying seismic data showed that our procedure performs as well as other procedures.