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Problem Being (problem + being)

Distribution by Scientific Domains

Medical Sciences	40%

Selected Abstracts

Some Learning Methods in Functional Networks

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 6 2000
Enrique Castillo
This article is devoted to learning functional networks. After a short introduction and motivation of functional networks using a CAD problem, four steps used in learning functional networks are described: (1) selection of the initial topology of the network, which is derived from the physical properties of the problem being modeled, (2) simplification of this topology, using functional equations, (3) estimation of the parameters or weights, using least squares and minimax methods, and (4) selection of the subset of basic functions leading to the best fit to the available data, using the minimum-description-length principle. Several examples are presented to illustrate the learning procedure, including the use of a separable functional network to recover the missing data of the significant wave height records in two different locations, based on a complete record from a third location where the record is complete. [source]

Contingent valuation in health care: does it matter how the ,good' is described?

HEALTH ECONOMICS, Issue 5 2008
Richard D. Smith
Abstract A general population sample of 104 Australian respondents completed an interviewer-administered contingent valuation (CV) survey that asked them to value five scenarios representing the same core improvement in health status. These scenarios varied only in the degree of narrative used to describe the condition causing the health problem being valued and labeling of this health problem. Results indicate no significant difference in willingness to pay (WTP) between expressing symptoms as a brief or moderate narrative, but a significantly lower WTP value when expressed in an extensive narrative. WTP also differed significantly according to condition ,labels'. Possible implications for CV research are outlined. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Previewing Policy Sciences: Multiple Lenses and Segmented Visions

POLITICS & POLICY, Issue 3 2006
Thomas A.P. Sinclair
In setting an agenda for policy sciences, Harold Lasswell argued that the field would be shaped by its contextuality, problem orientation, and methodological diversity. The review of developments in the field in this article shows that scholars have divided into positivist and post-positivist orientations that employ multiple frameworks and models. I argue that theoretical diversity should be expected and welcomed given the complexity of policy processes and phenomena. The article encourages positivists and post-positivists alike to allow the problem being studied to drive analysis and to seek ways to integrate different theoretical and methodological approaches. [source]

"Developing Good Taste in Evidence": Facilitators of and Hindrances to Evidence-Informed Health Policymaking in State Government

THE MILBANK QUARTERLY, Issue 2 2008
CHRISTOPHER J. JEWELL
Context: Policymaking is a highly complex process that is often difficult to predict or influence. Most of the scholarship examining the role of research evidence in policymaking has focused narrowly on characteristics of the evidence and the interactions between scientists and government officials. The real-life context in which policymakers are situated and make decisions also is crucial to the development of evidence-informed policy. Methods: This qualitative study expands on other studies of research utilization at the state level through interviews with twenty-eight state legislators and administrators about their real-life experiences incorporating evidence into policymaking. The interviews were coded inductively into the following categories: (1) the important or controversial issue or problem being addressed, (2) the information that was used, (3) facilitators, and (4) hindrances. Findings: Hindrances to evidence-informed policymaking included institutional features; characteristics of the evidence supply, such as research quantity, quality, accessibility, and usability; and competing sources of influence, such as interest groups. The policymakers identified a number of facilitators to the use of evidence, including linking research to concrete impacts, costs, and benefits; reframing policy issues to fit the research; training to use evidence-based skills; and developing research venues and collaborative relationships in order to generate relevant evidence. Conclusions: Certain hindrances to the incorporation of research into policy, like limited budgets, are systemic and not readily altered. However, some of the barriers and facilitators of evidence-informed health policymaking are amenable to change. Policymakers could benefit from evidence-based skills training to help them identify and evaluate high-quality information. Researchers and policymakers thus could collaborate to develop networks for generating and sharing relevant evidence for policy. [source]

Highest Density Difference Region Estimation with Application to Flow Cytometric Data

BIOMETRICAL JOURNAL, Issue 3 2009
Tarn Duong
Abstract Motivated by the needs of scientists using flow cytometry, we study the problem of estimating the region where two multivariate samples differ in density. We call this problem highest density difference region estimation and recognise it as a two-sample analogue of highest density region or excess set estimation. Flow cytometry samples are typically in the order of 10,000 and 100,000 and with dimension ranging from about 3 to 20. The industry standard for the problem being studied is called Frequency Difference Gating, due to Roederer and Hardy (2001). After couching the problem in a formal statistical framework we devise an alternative estimator that draws upon recent statistical developments such as patient rule induction methods. Improved performance is illustrated in simulations. While motivated by flow cytometry, the methodology is suitable for general multivariate random samples where density difference regions are of interest. [source]