The Lack of Memory Property in the Density Form for the Bivariate Setup

The lack of memory property is a characterizing property of the exponential distribution in the continuous domain. In the bivariate setup different generalizations of the same are available in terms of survival function. We extend this lack of memory property in terms of bivariate probability density function and examine its characterization properties. In this process the density version of the lack of memory property can be interlinked with conditionally specified exponential distribution, bivariate reciprocal coordinate subtangent of the density curve and a few other derived measures.     

Estimation and Classification of BOLD Responses Over Multiple Trials

In this article, we model functional magnetic resonance imaging (fMRI) data for event-related experiment data using a fourth degree spline to fit voxel specific blood oxygenation level-dependent (BOLD) responses. The data are preprocessed for removing long term temporal components such as drifts using wavelet approximations. The spatial dependence is incorporated in the data by the application of 3D Gaussian spatial filter. The methodology assigns an activation score to each trial based on the voxel specific characteristics of the response curve. The proposed procedure has a capability of being fully automated and it produces activation images based on overall scores assigned to each voxel. The methodology is illustrated on real data from an event-related design experiment of visually guided saccades (VGS).     

Hypothesis testing, power and sample size determination for between group comparisons in fMRI experiments

Modern methods for imaging the human brain, such as functional magnetic resonance imaging (fMRI) present a range of challenging statistical problems. In this paper, we first develop a large sample based test for between group comparisons and use it to determine the necessary sample size in order to obtain a target power via simulation under various alternatives for a given pre-specified significance level. Both testing and sample size calculations are particularly critical for neuroscientists who use these new techniques, since each subject is expensive to image.     

A Simulation Comparison of Approximate Tests for Fixed Effects in Random Coefficients Growth Curve Models

Often, the response variables on sampling units are observed repeatedly over time. The sampling units may come from different populations, such as treatment groups. This setting is routinely modeled by a random coefficients growth curve model, and the techniques of general linear mixed models are applied to address the primary research aim. An alternative approach is to reduce each subject’s data to summary measures, such as within-subject averages or regression coefficients. One may then test for equality of means of the summary measures (or functions of them) among treatment groups. Here, we compare by simulation the performance characteristics of three approximate tests based on summary measures and one based on the full data, focusing mainly on accuracy of p-values. We find that performances of these procedures can be quite different for small samples in several different configurations of parameter values. The summary-measures approach performed at least as well as the full-data mixed models approach.     

The Effects of Misconceptions on the Properties of Friedman's Test

Friedman's test is a widely used rank-based alternative to the analysis of variance (ANOVA) F-test for identifying treatment differences in a randomized complete block design. Many texts provide incomplete or misleading information about when Friedman's test may be appropriately applied. We discuss the assumptions needed for the test and common misconceptions. We show via simulation that when the variance or skew of the treatment distributions differ, application of Friedman's test to detect differences in treatment location can result in Type I error probabilities larger than the nominal α, and even when α is unaffected, the power of the test can be less than expected.     

The governance of non-profit micro finance institutions: lessons from history

Microfinance is high on the public agenda, and better corporate governance has been identified as a key factor for enhancing the viability of the industry. However, recent literature on the subject struggles to identify the corporate governance mechanisms that influence the performance of the Micro Finance Institutions (MFIs). Guided by stakeholder and agency theories, this paper uses a historical parallel found in savings banks to present corporate governance lessons for MFIs, particularly non-profit MFIs, today. The findings indicate that monitoring by bank associations, depositors, donors, and local communities was important in securing the survival of savings banks. In addition, a willingness to expand their mission to serve wealthier customers alongside the poor helped the banks become financially viable. These findings could prompt a rethinking of microfinance governance, which stresses regulation, for-profit ownership, and traditional vertical board control. The paper argues that a broader and more stakeholder-based understanding of corporate governance is necessary. Moreover, the paper demonstrates that historical studies can provide governance lessons for today.     

Problem-based Learning: Potential and Implementation Issues

This study addresses the issues associated with the application of problem-based learning, examines the key design dimensions of a problem-based learning course and considers the implications of problem-based learning for learners and educators.     

Implementing the right project structure to achieve coopetitive innovation projects

This research focuses on the project structure used by coopetitors to achieve common innovation projects. Scholars have recently identified an original but complex project structure that they call the Coopetitive Project Team (CPT). However, other project structures can also be implemented by coopetitors to achieve innovation. Therefore, we address the following question: for which types of innovation projects is CPT appropriate? We argue that coopetitors need to use CPT for high-risk and high-cost projects when the aim is to develop radical innovation. CPT allows coopetitors not only to develop innovation capabilities through close resource and knowledge sharing but also to manage the risk of opportunism. Conversely, coopetitors should use another project structure, Separated Project Teams (SPTs), for low-cost and low-risk projects when the aim is to develop incremental innovation. The SPT design allows coopetitors both to achieve the goal of the project and to minimize the risk of opportunism. To confirm our assumptions, we studied the project portfolios of Airbus and Thales, two firms in the space satellite industry. Our findings confirm that coopetitors should implement CPTs to handle innovation projects that are costly, risky and highly innovative. CPTs permit the sharing of knowledge and the management of high opportunism risk, both of which are necessary to achieve radical innovation. Conversely, coopetitors rely on SPTs for low-cost projects that require a low degree of knowledge sharing, thus avoiding the risk of opportunism in achieving their incremental innovation objectives.