Literatura científica selecionada sobre o tema "OMOP common data model"

In September 2007 the national team members of the International Inforum (Interindustry Forecasting Project at the University of Maryland) group held the XV annual World Conference in Truijllo, Spain. Such Conferences offer the participants to report their achievements in the different fields concerning the macroeconomic multisectoral modeling approach and data development. The national partners build their country model based on a common input-output accounting structure and a similar econometric modeling approach for sectoral and macroeconomic variables. In each Conference, the contributions refer to the wide spectrum of research activities carried on within the Inforum system of models.

The book presents developmental outcomes from an EU Erasmus+ project involving eight partner universities in seven countries in Europe. Its focus is the development of mathematics teaching and learning at university level to enhance the learning of mathematics by university students. Its theoretical focus is inquiry-based teaching and learning. It bases all activity on a three-layer model of inquiry: (1) Inquiry in mathematics and in the learning of mathematics in lecture, tutorial, seminar or workshop, involving students and teachers; (2) Inquiry in mathematics teaching involving teachers exploring and developing their own practices in teaching mathematics; (3) Inquiry as a research process, analysing data from layers (1) and (2) to advance knowledge inthe field. As required by the Erasmus+ programme, it defines Intellectual Outputs (IOs) that will develop in the project. PLATINUM has six IOs: The Inquiry-based developmental model; Inquiry communities in mathematics learning and teaching; Design of mathematics tasks and teaching units; Inquiry-based professional development activity; Modelling as an inquiry process; Evalutation of inquiry activity with students. The project has developed Inquiry Communities, in each of the partner groups, in which mathematicians and educators work together in supportive collegial ways to promote inquiry processes in mathematics learning and teaching. Through involving students in inquiry activities, PLATINUM aims to encourage students` own in-depth engagement with mathematics, so that they develop conceptual understandings which go beyond memorisation and the use of procedures. Indeed the eight partners together have formed an inquiry community, working together to achieve PLATINUM goals within the specific environments of their own institutions and cultures. Together we learn from what we are able to achieve with respect to both common goals and diverse environments, bringing a richness of experience and learning to this important area of education. Inquiry communities enable participants to address the tensions and issues that emerge in developmental processes and to recognise the critical nature of the developmental process. Through engaging in inquiry-based development, partners are enabled and motivated to design activities for their peers, and for newcomers to university teaching of mathematics, to encourage their participation in new forms of teaching, design of teaching, and activities for students. Such professional development design is an important outcome of PLATINUM. One important area of inquiry-based activity is that of “modelling” in mathematics. Partners have worked together across the project to investigate the nature of modelling activities and their use with students. Overall, the project evaluates its activity in these various parts to gain insights to the sucess of inquiry based teaching, learning and development as well as the issues and tensions that are faced in putting into practice its aims and goals.

This chapter explores several of the most common and useful approaches to atmospheric data fitting as well as the process of using air mass factors to produce vertical atmospheric column abundances from line-of-sight slant columns determined by data fitting. An atmospheric spectrum or other type of atmospheric sounding is usually fitted to a parameterized physical model by minimizing a cost function, usually chi-squared. Linear fitting, when the model of the measurements is linear in the model parameters is described, followed by the more common nonlinear fitting case. For nonlinear fitting, the standard Levenberg-Marquardt method is described, followed by the use of optimal estimation, one of several retrieval methods that make use of a priori information to providing regularization for the solution. In the context of optimal estimation, weighting functions, contribution functions, and averaging kernels are described. The Twomey-Tikhonov regularization procedure is presented. Correlated parameters, with the important example of Earth’s atmospheric ozone, are discussed.

This chapter introduces embedded models. This is a special case of a parametric model which cannot be obtained simply by setting the parameters to particular values in a simple way. An example is the regression function y = b[1−exp(−ax)], which is always curved when a and b have fixed values. But letting a tend to zero and b tend to infinity simultaneously, whilst keeping ab = c fixed, yields y = cx, a straight-line special case. When this is the true model, fitting the original two-parameter model leads to very unstable and individually meaningless estimates of a and b. Such embedded models are actually very common in the literature, leading to confusion in interpretation of results when undetected. In this chapter, embeddedness is defined and a large number of regression embedded model examples given. Detection and removal of embeddedness by reparametrization is discussed. Two real data numerical examples are given.

This chapter focuses upon the needs of two approaches, economic evaluations based on decision analytic models, and those alongside clinical trials in terms of the collection and analysis of health state values. The first section of the chapter presents requirements that are likely to be common to any study in which health state values are collected from patients and/or members of the general population, including: who to ask, mode of administration, timing of assessments, sample size, and handling uncertainty. The second section of the chapter considers issues specific to trial-based economic evaluations, and the final section considers issues specific to the design and analysis of health state valuation data for economic models.

This chapter discusses recovering shape or structural information from SAXS data. Key to any such process is the ability to generate a calculated intensity from a model, and to compare this curve with the experimental one. Models for the particle scattering density can be approximated as pure homogenenous geometric shapes. More complex particle surfaces can be represented by spherical harmonics or by a set of close-packed beads. Sometimes structural information is known for components of a particle. Rigid body modeling attempts to rotate and translate structures relative to one another, such that the resulting scattering profile calculated from the model agrees with the experimental SAXS data. More advanced hybrid modelling procedures aim to incorporate as much structural information as is available, including modelling protein dynamics. Solutions may not always contain a homogeneous set of particles. A common case is the presence of two or more conformations of a single particle or a mixture of oligomeric species. The method of singular value decomposition can extract scattering for conformationally distinct species.

This article discusses the use of functional coefficient models for economic and financial data analysis. It first provides an overview of recent developments in the nonparametric estimation and testing of functional coefficient models, with particular emphasis on the kernel local polynomial smoothing method, before considering misspecification testing as an important econometric question when fitting a functional (varying) coefficient model or a trending time-varying coefficient model. It then describes two major real-life applications of functional coefficient models in economics and finance: the first deals with the use of functional coefficient instrumental-variable models to investigate the empirical relation between wages and education in a random sample of young Australian female workers from the 1985 wave of the Australian Longitudinal Survey, and the second is concerned with the use of functional coefficient beta models to analyze the common stock price of Microsoft stock (MSFT) during the year 2000 using the daily closing prices.