The educational orientation of participants in postgraduate managerial study programmes in Poland

The health care industry in Taiwan has faced many challenges in recent years. To tackle those challenges, many hospitals in Taiwan began using management techniques used in other industries. As a result, quality management has become important within Taiwan's health care industry. This study provides a perspective on how this health care industry, by developing both quality control circle (QCC) practices and organizational learning techniques, responded to those challenges. In this study, the authors conducted a comprehensive review of studies published between 1995 and 2007 in Taiwan. The review focused on the quality management, QCC, organizational learning and health care organization aspects reported in previous studies. The authors applied the 4Is (intuiting, interpreting, integrating and institutionalizing) conceptual framework of Crossan et al. (1999 Crossan, M. M., Lane, H. W. and White, R. E. 1999. An organizational learning framework: From intuition to institution. Academy of Management Review, 24(3): 522–537. [Crossref], [Web of Science ®] , [Google Scholar]) (Crossan, M.M., H.W. Lane, and R.E. White. 1999 Crossan, M. M., Lane, H. W. and White, R. E. 1999. An organizational learning framework: From intuition to institution. Academy of Management Review, 24(3): 522–537. [Crossref], [Web of Science ®] , [Google Scholar]. An organizational learning framework: From intuition to institution. Academy of Management Review 24, no. 3: 522–37). in their discussion of the linkage between quality improvement activities and organizational learning. The results revealed the existence of a link between organizational learning and QCC activities. In addition, the reviewed QCC activities facilitated organizational learning and contributed to health care quality improvements in the studied hospitals. Finally, QCC practices in hospitals facilitated dissemination of organizational learning concepts in those hospitals.     

The role of multi-objective optimization in negotiation and mediation support

The paper reviews the methodology of multi-objective modeling and optimization used in decision support based on computerized analytical models (as opposed to logical models used in expert systems) that represent expert knowledge in a given field. The essential aspects of this methodology relate to its flexibility: modeling and optimization methods are treated not as goals in themselves but as tools that help a sovereign user (an analyst or a decision maker) to interact with the model, to generate and analyze various decision options, to learn about possible outcomes of these decisions. Although the applications of such methods in negotiation and mediation support is scarce yet, their flexibility increases essentially the chances of such applications. Various aspects of negotiation and mediation methods related to multi-objective optimization and game theory are also reviewed. A possible application of the MCBARG system for supporting negotiation related to the acid rain problem is briefly summarized.     

An $$O(n\log n)$$ algorithm for finding edge span of cacti

Let \(G=(V,E)\) be a nonempty graph and \(\xi :E\rightarrow \mathbb {N}\) be a function. In the paper we study the computational complexity of the problem of finding vertex colorings \(c\) of \(G\) such that:

1. (1)
   \(|c(u)-c(v)|\ge \xi (uv)\) for each edge \(uv\in E\);
    
2. (2)
   the edge span of \(c\), i.e. \(\max \{|c(u)-c(v)|:uv\in E\}\), is minimal.
    

We show that the problem is NP-hard for subcubic outerplanar graphs of a very simple structure (similar to cycles) and polynomially solvable for cycles and bipartite graphs. Next, we use the last two results to construct an algorithm that solves the problem for a given cactus \(G\) in \(O(n\log n)\) time, where \(n\) is the number of vertices of \(G\).

     

Testing Hypotheses for Variance Components in Mixed Linear Models

In the paper the problem of testing hypotheses for variance components in mixed linear models is considered. It is assumed that covariance matrices commute after using the usual invariance procedure with respect to the group of translations. The test for vanishing of single variance component is based on the locally best quadratic unbiased estimator of this component and rejects hypothesis if the ratio of positive and negative part of this estimator is sufficiently large. The power of this test with powers of other four tests for two-way classification models corresponding to block design is compared.     

Estimation for Stochastic Models Driven by Laplace Motion

Laplace motion is a Lévy process built upon Laplace distributions. Non Gaussian stochastic fields that are integrals with respect to this process are considered and methods for their model fitting are discussed. The proposed procedures allow for inference about the parameters of the underlying Laplace distributions. A fit of dependence structure is also addressed. The importance of a convenient parameterization that admits natural and consistent estimation for this class of models is emphasized. Several parameterizations are introduced and their advantages over one another discussed. The proposed estimation method targets the standard characteristics: mean, variance, skewness and kurtosis. Their sample equivalents are matched in the closest possible way as allowed by natural constraints within this class. A simulation study and an example of potential applications conclude the article.     

Recursive improvement of estimates in a Gauss-Markov model with linear restrictions

The minimum-dispersion linear unbiased estimator of a set of estimable functions in a general Gauss-Markov model with double linear restrictions is considered. The attention is focused on developing a recursive formula in which an initial estimator, obtained from the unrestricted model, is corrected with respect to the restrictions successively incorporated into the model. The established formula generalizes known results developed for the simple Gauss-Markov model.     

Maximum variance of order statistics from symmetric populations revisited

We consider i.i.d. samples of size n with symmetric non-degenerate parent distributions and finite variances. Papadatos [A note on maximum variance of order statistics from symmetric populations, Ann. Inst. Statist. Math. 48 (1997), pp. 117–121] proved that the maximal variance of each non-extreme order statistic, expressed in the population variance units, is attained in a one-parametric family of symmetric two- and three-point distributions. The parameters of the extreme variance distributions coincide with the arguments maximizing some polynomials of degree 2n?1 over a finite interval. The bounds for variances are equal to the maximal values of the polynomials. We present a more precise solution to the problem by applying the variation diminishing property of Bernstein polynomials.