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Relevant bibliographies by topics / OMOP common data model

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Academic literature on the topic 'OMOP common data model'

Author: Grafiati

Published: 25 January 2025

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Books on the topic "OMOP common data model"

1

Gavin, William T. A common model approach to macroeconomics: Using panel data to reduce sampling error. Federal Reserve Bank of St. Louis, 2003.

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2

Grassini, Maurizio, and Rossella Bardazzi, eds. Structural changes, international trade and multisectoral modelling. Firenze University Press, 2008. http://dx.doi.org/10.36253/978-88-8453-740-9.

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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

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3

Jaworski, Barbara, Josef Rebenda, Reinhard Hochmuth, et al. Inquiry in University Mathematics Teaching and Learning. Masaryk University Press, 2021. http://dx.doi.org/10.5817/cz.muni.m210-9983-2021.

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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 exp

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4

Designing a Common Interchange Format for Unit Data Using the Command and Control Information Exchange Data Model (C2IEDM) and XSLT. Storming Media, 2004.

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5

Munn, Michael, Sara Robinson, and Valliappa Lakshmanan. Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps. O'Reilly Media, Incorporated, 2020.

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6

Chance, Kelly, and Randall V. Martin. Data Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199662104.003.0011.

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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, t

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7

Cheng, Russell. Embedded Model Problem. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0005.

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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 a

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8

Brazier, John, Julie Ratcliffe, Joshua A. Salomon, and Aki Tsuchiya. Design and analysis of health state valuation data for model-based economic evaluations and for economic evaluations alongside clinical trials. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198725923.003.0009.

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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

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9

Lattman, Eaton E., Thomas D. Grant, and Edward H. Snell. Shape Reconstructions from Small Angle Scattering Data. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199670871.003.0004.

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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 t

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10

Cai, Zongwu. Functional Coefficient Models for Economic and Financial Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.6.

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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

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