Two-step calibration of design weights under two auxiliary variables in sample survey

Calibration on the available auxiliary variables is widely used to increase the precision of the estimates of parameters. Singh and Sedory [Two-step calibration of design weights in survey sampling. Commun Stat Theory Methods. 2016;45(12):3510–3523.] considered the problem of calibration of design weights under two-step for single auxiliary variable. For a given sample, design weights and calibrated weights are set proportional to each other, in the first step. While, in the second step, the value of proportionality constant is determined on the basis of objectives of individual investigator/user for, for example, to get minimum mean squared error or reduction of bias. In this paper, we have suggested to use two auxiliary variables for two-step calibration of the design weights and compared the results with single auxiliary variable for different sample sizes based on simulated and real-life data set. The simulated and real-life application results show that two-auxiliary variables based two-step calibration estimator outperforms the estimator under single auxiliary variable in terms of minimum mean squared error.     

Critical Success Factors for Strategic Alliances in the Information Technology Industry: An Empirical Study*

Intense competition in the marketplace is forcing organizations to examine different ways by which they could enhance or retain their competitive edge. Strategic alliance is one such option through which an organization can leverage its resources to emerge as an effective competitor. Such alliances are burgeoning in the information technology industry, which is characterized by rapid change and short innovation cycles. Successful management of alliances has proved to be very elusive for the most part, and there is a lack of theoretical and empirical work in this area. Literature on critical factors was synthesized, and then a parsimonious set of critical factors was identified using a combination of interview and survey methodologies. Our study has implications for both practice and future research.     

Random field models in image analysis*

Image models are useful in quantitatively specifying natural constraints and general assumptions about the physical world and the imaging process. This review paper explains how Gibbs and Markov random field models provide a unifying theme for many contemporary problems in image analysis. Random field models permit the introduction of spatial context into pixel labeling problems, such as segmentation and restoration. Random field models also describe textured images and lead to algorithms for generating textured images, classifying textures and segmenting textured images. In spite of some impressive model-based image restoration and texture segmentation results reported in the literature, a number of fundamental issues remain unexplored, such as the specification of MRF models, modeling noise processes, performance evaluation, parameter estimation, the phase transition phenomenon and the comparative analysis of alternative procedures. The literature of random field models is filled with great promise, but a better mathematical understanding of these issues is needed as well as efficient algorithms for applications. These issues need to be resolved before random field models will be widely accepted as general tools in the image-processing community.     

Random field models in image analysis

Image models are useful in quantitatively specifying natural constraints and general assumptions about the physical world and the imaging process. This review paper explains how Gibbs and Markov random field models provide a unifying theme for many contemporary problems in image analysis. Random field models permit the introduction of spatial context into pixel labeling problems, such as segmentation and restoration. Random field models also describe textured images and lead to algorithms for generating textured images, classifying textures, and segmenting textured images. In spite of some impressive model-based image restoration and texture segmentation results reported in the literature, a number of fundamental issues remain unexplored, such as the specification of MRF models, modeling noise processes, performance evaluation, parameter estimation, the phase transition phenomenon, and the comparative analysis of alternative procedures. The literature of random field models is filled with great promise, but a better mathematical understanding of these issues is needed as well as efficient algorithms for applications. These issues need to be resolved before random field models will be widely accepted as general tools in the image processing community.     

Does “fighting back” make a difference? The case of the Canadian auto workers union

Union revitalization requires mutually reinforcing efforts in organizing, political action, and collective bargaining. We examine the experience of the Canadian Auto Workers (CAW). In addition to membership growth, three bargaining outcomes are analyzed: (1) wage gains; (2) the ability to resist a trend towards longer duration collective agreements; and, (3) success in negotiating same-sex spousal recognition. In each of these areas the CAW has articulated a policy and then made gains in both absolute and relative terms. The CAW’s renewal strategy links these bargaining goals with social activism and membership growth through mergers and new organizing.     

Estimation of a linear transformation and an associated distributional problem

A multivariate “errors in variables” regression model is proposed which generalizes a model previously considered by Gleser and Watson (1973). Maximum likelihood estimators [MLE's] for the parameters of this model are obtained, and the consistency properties of these estimators are investigated. Distribution of the MLE of the “error” variance is obtained in a simple case while the mean and the variance of the estimator are obtained in this case without appealing to the exact distribution.     

Non-parametric Tests for the Comparison of Point Processes Based on Incomplete Data

We consider the comparison of point processes in a discrete observation situation in which each subject is observed only at discrete time points and no history information between observation times is available. A class of non-parametric test statistics for the comparison of point processes based on this kind of data is presented and their asymptotic distributions are derived. The proposed tests are generalizations of the corresponding tests for continuous observations. Some results from a simulation study for evaluating the proposed tests are presented and an illustrative example from a clinical trial is discussed.     

Quality Testing and Product Rationing by Input Suppliers

Quality testing by suppliers has significant ramifications for downstream supply chain participants and retail consumers. This article focuses on such implications accounting for the fact that suppliers often enjoy discretion in quality testing and reporting. Under a discretionary testing and reporting environment, we show that a supplier can improve the market's perception of product quality by engaging in self‐imposed production cuts. Production cuts dampen supplier incentives to engage in excessive quality testing, putting the supplier and the market on a more equal information footing. This reduces the market's need to skeptically discount product quality to protect itself. The improved market perception, then, reduces quality testing demand, introducing cost savings. The result that costly production cuts can improve quality perceptions indicates that the groundwork for influencing market perceptions may have to be laid upfront, even prior to acquiring private information, providing a contrast to routine signaling models.     

Exploring literacy in Social Work Education: a social practices approach to student writing

The literacy of social workers has been highlighted as a concern by the strengthening of both entry and assessment literacy requirements of the new social work degree in the United Kingdom. This paper challenges the traditional perception of student writing, the specific focus of this paper, as being associated with basic literacy and skill acquisition, by presenting a ‘social practices’ approach to student writing in social work education. Whilst in no way contradicting the belief that literacy is central to professional social work competence, and thus an essential aspect of social work education, this approach provides a social and cultural framework for educators to reflect upon what it means for students to engage in writing in social work courses. The paper focuses in particular on meaning making experiences of non‐traditional (including Black and Minority Ethnic) students engaging in academic writing. The paper is based upon research which explores the experiences of a socially and ethnically diverse group of social work students engaged in academic writing on a diploma in social work programme. Whilst representing work in development, it suggests that the recognition of issues such as language history and identity are of significant importance for social work programmes striving to ensure that non‐traditional students are not disadvantaged in their academic writing.