Distribution-free tests in randomized blocks

Classes of distribution-free tests are proposed for testing homogeneity against order restricted as well as unrestricted alternatives in randomized block designs with multiple observations per cell. Allowing for different interblock scoring schemes, these tests are constructed based on the method of within block rankings. Asymptotic distributions (cell sizes tending to infinity) of these tests are derived under the assumption of homogeneity. The Pitman asymptotic relative efficiencies relative to the least squares statistics are studied. It is shown that when blocks are governed by different distributions, adaptive choice of scores within each block results in asymptotically more efficient tests as compared with methods that ignore such information. Monte Carlo simulations of selected designs indicate that the method of within block rankings is more power robust with respect to differing block distributions.     

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.     

Exercise training in thermo-mineral spring water has beneficial effects on hemodynamic and health-related factors in young-older hypertensive women: A randomized control trial

ABSTRACT

The aim of the study was to evaluate the effects of an 8-week aerobic training in mineral geothermal water on hemodynamic variables, VO_2max, and body composition in sedentary hypertensive women. Twenty postmenopausal women (58.55 ± 3.28 years) were divided into an exercise group (2 days/week, 30–40 minutes, 60%–75% of HR_max) and control group. Compared to the control group, a signi?cant reduction in systolic blood pressure, heart rate, rate pressure product, body fat percent, and a signi?cant improvement in VO_2max values was found. Aerobic training in thermo-mineral spring water is a safe and effective training modality in the young-older hypertensive population.     

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.     

Determinants of Profitability of Banking Sector in India

India currently is one of the fastest growing economies in the world. One sector of Indian economy that has played a critical role in transforming the Indian economy has been its banking sector. But this sector of Indian economy has also gone through a major transition that is still in progress. Many events and policies have contributed in this transition. One pivotal variable has been the growing profitability of this sector in the recent years. But profitability in banking sector is affected by numerous factors. These factors can be internal or external. In this research we shall try to examine the most important factors that may stem from both internal and external factors, which affect profitability of Indian banking sector. For this study a balanced panel data set is used that is drawn from Indian banking industry. The data is compiled for the purpose of investigating the nature of the relationship between the profitability and the factors that determine profitability of banks in India. The results of the study clearly demonstrate a close correlation between both internal and external factors and the level of profitability of banks in India.     

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.