Mutiple three-decision procedure for comparing several exponential treatments with the best control

In this paper, the three-decision procedures to classify p treatments as better than or worse than one control, proposed for normal/symmetric probability models [Bohrer, Multiple three-decision rules for parametric signs. J. Amer. Statist. Assoc. 74 (1979), pp. 432–437; Bohrer et al., Multiple three-decision rules for factorial simple effects: Bonferroni wins again!, J. Amer. Statist. Assoc. 76 (1981), pp. 119–124; Liu, A multiple three-decision procedure for comparing several treatments with a control, Austral. J. Statist. 39 (1997), pp. 79–92 and Singh and Mishra, Classifying logistic populations using sample medians, J. Statist. Plann. Inference 137 (2007), pp. 1647–1657]; in the literature, have been extended to asymmetric two-parameter exponential probability models to classify p(p≥1) treatments as better than or worse than the best of q(q≥1) control treatments in terms of location parameters. Critical constants required for the implementation of the proposed procedure are tabulated for some pre-specified values of probability of no misclassification. Power function of the proposed procedure is also defined and a common sample size necessary to guarantee various pre-specified power levels are tabulated. Optimal allocation scheme is also discussed. Finally, the implementation of the proposed methodology is demonstrated through numerical example.     

Multiple Comparisons with More than One Control for Exponential Location Parameters Under Heteroscedasticity

In this article we present a simple bootstrap method for time series. The proposed method is model-free, and hence it enables us to avoid certain situations where the bootstrap samples may contain impossible values due to resampling from the residuals. The method is easy to implement and can be applied to stationary and nonstationary time series. The simulation results and the application to real time series data show that the method works very well.     

Bayesian estimation of sensitivity level and population proportion of a sensitive characteristic in a binary optional unrelated question RRT model

Sihm et al. (2016 Sihm, J. S., A. Chhabra, and S. N. Gupta. 2016. An optional unrelated question RRT model. Involve: A Journal of Mathematics 9 (2):195–209.[Crossref] , [Google Scholar]) proposed an unrelated question binary optional randomized response technique (RRT) model for estimating the proportion of population that possess a sensitive characteristic and the sensitivity level of the question. In our work, decision theoretic approach has been followed to obtain Bayes estimates of the two parameters along with their corresponding minimal Bayes posterior expected losses (BPEL) using beta prior and squared error loss function (SELF). Relative losses are also examined to compare the performances of the Bayes estimates with those of the classical estimates obtained by Sihm et al. (2016 Sihm, J. S., A. Chhabra, and S. N. Gupta. 2016. An optional unrelated question RRT model. Involve: A Journal of Mathematics 9 (2):195–209.[Crossref] , [Google Scholar]). The results obtained are illustrated with the help of real survey data using non informative prior.     

Migrant Selection and the Health of U.S. Immigrants From the Former Soviet Union

Few prior studies have investigated the health of U.S. immigrants from the former Soviet Union (FSU). Utilizing data from the 2000 U.S. census and the 2000–2007 National Health Interview Survey (NIHS), we compare levels of disability of FSU immigrants with U.S.-born whites (ages 50–84). Our findings suggest an “epidemiologic paradox” in that FSU immigrants possess higher levels of education compared with U.S.-born whites, but report considerably higher disability with and without adjustment for education. Nonetheless, FSU immigrants report lower levels of smoking and heavy alcohol use compared with U.S.-born whites. We further investigate disability by period of arrival among FSU immigrants. Changes in Soviet emigration policies conceivably altered the level of health selectivity among émigrés. We find evidence that FSU immigrants who emigrated during a period when a permission to emigrate was hard to obtain (1970–1986) displayed less disability compared with those who emigrated when these restrictions were less stringent (1987–2000). Finally, we compare disability among Russian-born U.S. immigrants with that of those residing in Russia as a direct test of health selectivity. We find that Russian immigrants report lower levels of disability compared with Russians in Russia, suggesting that they are positively selected for health despite their poor health relative to U.S.-born whites.     

Partial degree bounded edge pacing problem for graphs and $$$$-uniform hypergraphs

Given a graph \(G=(V,E)\) and a non-negative integer \(c_u\) for each \(u\in V\), partial degree bounded edge packing problem is to find a subgraph \(G^{\prime }=(V,E^{\prime })\) with maximum \(|E^{\prime }|\) such that for each edge \((u,v)\in E^{\prime }\), either \(deg_{G^{\prime }}(u)\le c_u\) or \(deg_{G^{\prime }}(v)\le c_v\). The problem has been shown to be NP-hard even for uniform degree constraint (i.e., all \(c_u\) being equal). In this work we study the general degree constraint case (arbitrary degree constraint for each vertex) and present two combinatorial approximation algorithms with approximation factors \(4\) and \(2\). Then we give a \(\log _2 n\) approximation algorithm for edge-weighted version of the problem and an efficient exact algorithm for edge-weighted trees with time complexity \(O(n\log n)\). We also consider a generalization of this problem to \(k\)-uniform hypergraphs and present a constant factor approximation algorithm based on linear programming using Lagrangian relaxation.     

Training foster parents in loyalty conflict: A training evaluation

Fifty two foster parents participated in one of seven offerings of a two-and-a-half hour training on the issue of children caught in a loyalty conflict. Prior to the training foster parents completed a measure of their demographics, receptivity to learning about the issue, and their knowledge about the issue. Following the training the participants completed a measure of their perception that the training was helpful, their intent to change based on the training, and their knowledge about loyalty conflicts. They also participated in a focused discussion about loyalty conflicts. In this sample foster parents were found to have high levels of receptivity to training, high rates of perceived helpfulness of the training, and high levels of intent to change after the training as well as an increase in knowledge regarding loyalty conflict from before to after the training. Despite limitations in sampling strategy and sample size, the data support the belief that foster parents want to learn about this issue and can benefit from a single training on it.     

Challenges and opportunities of business process outsourcing in India

We identify managerial challenges and opportunities faced by business process outsourcing (BPO) firms in India by interviewing 28 executives in lower (n = 10), middle (n = 11), and upper management (n = 7) in 15 BPO firms. Content analysis of the responses revealed human resources and organizationrelated challenges as the most critical issues. SWOT (Strengths, Weaknesses, Opportu-nities, Threats) analysis uncovered future opportunities, e.g., Knowledge Process Outsourcing. Secondary data, from four global research agencies and a national trade association, supported our findings. Organizational changes to address challenges and to exploit opportunities are explored.