The Experts below are selected from a list of 976524 Experts worldwide ranked by ideXlab platform
Ruth Davies - One of the best experts on this subject based on the ideXlab platform.
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a taxonomy of Model Structures for economic evaluation of health technologies
Health Economics, 2006Co-Authors: A. Brennan, S.e. Chick, Ruth DaviesAbstract:Models for the economic evaluation of health technologies provide valuable information to decision makers. The choice of Model structure is rarely discussed in published studies and can affect the results produced. Many papers describe good Modelling practice, but few describe how to choose from the many types of available Models. This paper develops a new taxonomy of Model Structures. The horizontal axis of the taxonomy describes assumptions about the role of expected values, randomness, the heterogeneity of entities, and the degree of non-Markovian structure. Commonly used aggregate Models, including decision trees and Markov Models require large population numbers, homogeneous sub-groups and linear interactions. Individual Models are more flexible, but may require replications with different random numbers to estimate expected values. The vertical axis of the taxonomy describes potential interactions between the individual actors, as well as how the interactions occur through time. Models using interactions, such as system dynamics, some Markov Models, and discrete event simulation are fairly uncommon in the health economics but are necessary for Modelling infectious diseases and systems with constrained resources. The paper provides guidance for choosing a Model, based on key requirements, including output requirements, the population size, and system complexity. Copyright © 2006 John Wiley & Sons, Ltd.
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A taxonomy of Model Structures for economic evaluation of health technologies
Health Economics, 2006Co-Authors: A. Brennan, S.e. Chick, Ruth DaviesAbstract:Models for the economic evaluation of health technologies provide valuable information to decision makers. The choice of Model structure is rarely discussed in published studies and can affect the results produced. Many papers describe good Modelling practice, but few describe how to choose from the many types of available Models. This paper develops a new taxonomy of Model Structures. The horizontal axis of the taxonomy describes assumptions about the role of expected values, randomness, the heterogeneity of entities, and the degree of non-Markovian structure. Commonly used aggregate Models, including decision trees and Markov Models require large population numbers, homogeneous sub-groups and linear interactions. Individual Models are more flexible, but may require replications with different random numbers to estimate expected values. The vertical axis of the taxonomy describes potential interactions between the individual actors, as well as how the interactions occur through time. Models using interactions, such as system dynamics, some Markov Models, and discrete event simulation are fairly uncommon in the health economics but are necessary for Modelling infectious diseases and systems with constrained resources. The paper provides guidance for choosing a Model, based on key requirements, including output requirements, the population size, and system complexity.
A. Brennan - One of the best experts on this subject based on the ideXlab platform.
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a taxonomy of Model Structures for economic evaluation of health technologies
Health Economics, 2006Co-Authors: A. Brennan, S.e. Chick, Ruth DaviesAbstract:Models for the economic evaluation of health technologies provide valuable information to decision makers. The choice of Model structure is rarely discussed in published studies and can affect the results produced. Many papers describe good Modelling practice, but few describe how to choose from the many types of available Models. This paper develops a new taxonomy of Model Structures. The horizontal axis of the taxonomy describes assumptions about the role of expected values, randomness, the heterogeneity of entities, and the degree of non-Markovian structure. Commonly used aggregate Models, including decision trees and Markov Models require large population numbers, homogeneous sub-groups and linear interactions. Individual Models are more flexible, but may require replications with different random numbers to estimate expected values. The vertical axis of the taxonomy describes potential interactions between the individual actors, as well as how the interactions occur through time. Models using interactions, such as system dynamics, some Markov Models, and discrete event simulation are fairly uncommon in the health economics but are necessary for Modelling infectious diseases and systems with constrained resources. The paper provides guidance for choosing a Model, based on key requirements, including output requirements, the population size, and system complexity. Copyright © 2006 John Wiley & Sons, Ltd.
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A taxonomy of Model Structures for economic evaluation of health technologies
Health Economics, 2006Co-Authors: A. Brennan, S.e. Chick, Ruth DaviesAbstract:Models for the economic evaluation of health technologies provide valuable information to decision makers. The choice of Model structure is rarely discussed in published studies and can affect the results produced. Many papers describe good Modelling practice, but few describe how to choose from the many types of available Models. This paper develops a new taxonomy of Model Structures. The horizontal axis of the taxonomy describes assumptions about the role of expected values, randomness, the heterogeneity of entities, and the degree of non-Markovian structure. Commonly used aggregate Models, including decision trees and Markov Models require large population numbers, homogeneous sub-groups and linear interactions. Individual Models are more flexible, but may require replications with different random numbers to estimate expected values. The vertical axis of the taxonomy describes potential interactions between the individual actors, as well as how the interactions occur through time. Models using interactions, such as system dynamics, some Markov Models, and discrete event simulation are fairly uncommon in the health economics but are necessary for Modelling infectious diseases and systems with constrained resources. The paper provides guidance for choosing a Model, based on key requirements, including output requirements, the population size, and system complexity.
Roland Toth - One of the best experts on this subject based on the ideXlab platform.
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data driven predictive control based on orthonormal basis functions
Conference on Decision and Control, 2015Co-Authors: A A Bachnas, S Weiland, Roland TothAbstract:This paper presents a concept of an adaptive Model predictive control (MPC) scheme based on a flexible predictor Model that utilizes orthonormal basis functions (OBFs). This Model structure offers a trade-off between adaptation of the Model accuracy in terms of the expansion coefficients and the dynamical structure in terms of the basis functions. We show that this adaptation can maintain desirable control performance. Moreover, since OBF Model Structures can be seen as a generalization of finite impulse response (FIR) Model Structures, the incorporation of this scheme in FIR-based MPC is rather straightforward.
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a bias corrected estimator for nonlinear systems with output error type Model Structures
Automatica, 2014Co-Authors: Dario Piga, Roland TothAbstract:Parametric identification of linear time-invariant (LTI) systems with output-error (OE) type of noise Model Structures has a well-established theoretical framework. Different algorithms, like instrumental-variables based approaches or prediction error methods (PEMs), have been proposed in the literature to compute a consistent parameter estimate for linear OE systems. Although the prediction error method provides a consistent parameter estimate also for nonlinear output-error (NOE) systems, it requires to compute the solution of a nonconvex optimization problem. Therefore, an accurate initialization of the numerical optimization algorithms is required, otherwise they may get stuck in a local minimum and, as a consequence, the computed estimate of the system might not be accurate. In this paper, we propose an approach to obtain, in a computationally efficient fashion, a consistent parameter estimate for output-error systems with polynomial nonlinearities. The performance of the method is demonstrated through a simulation example.
S.e. Chick - One of the best experts on this subject based on the ideXlab platform.
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a taxonomy of Model Structures for economic evaluation of health technologies
Health Economics, 2006Co-Authors: A. Brennan, S.e. Chick, Ruth DaviesAbstract:Models for the economic evaluation of health technologies provide valuable information to decision makers. The choice of Model structure is rarely discussed in published studies and can affect the results produced. Many papers describe good Modelling practice, but few describe how to choose from the many types of available Models. This paper develops a new taxonomy of Model Structures. The horizontal axis of the taxonomy describes assumptions about the role of expected values, randomness, the heterogeneity of entities, and the degree of non-Markovian structure. Commonly used aggregate Models, including decision trees and Markov Models require large population numbers, homogeneous sub-groups and linear interactions. Individual Models are more flexible, but may require replications with different random numbers to estimate expected values. The vertical axis of the taxonomy describes potential interactions between the individual actors, as well as how the interactions occur through time. Models using interactions, such as system dynamics, some Markov Models, and discrete event simulation are fairly uncommon in the health economics but are necessary for Modelling infectious diseases and systems with constrained resources. The paper provides guidance for choosing a Model, based on key requirements, including output requirements, the population size, and system complexity. Copyright © 2006 John Wiley & Sons, Ltd.
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A taxonomy of Model Structures for economic evaluation of health technologies
Health Economics, 2006Co-Authors: A. Brennan, S.e. Chick, Ruth DaviesAbstract:Models for the economic evaluation of health technologies provide valuable information to decision makers. The choice of Model structure is rarely discussed in published studies and can affect the results produced. Many papers describe good Modelling practice, but few describe how to choose from the many types of available Models. This paper develops a new taxonomy of Model Structures. The horizontal axis of the taxonomy describes assumptions about the role of expected values, randomness, the heterogeneity of entities, and the degree of non-Markovian structure. Commonly used aggregate Models, including decision trees and Markov Models require large population numbers, homogeneous sub-groups and linear interactions. Individual Models are more flexible, but may require replications with different random numbers to estimate expected values. The vertical axis of the taxonomy describes potential interactions between the individual actors, as well as how the interactions occur through time. Models using interactions, such as system dynamics, some Markov Models, and discrete event simulation are fairly uncommon in the health economics but are necessary for Modelling infectious diseases and systems with constrained resources. The paper provides guidance for choosing a Model, based on key requirements, including output requirements, the population size, and system complexity.
Vazken Andreassian - One of the best experts on this subject based on the ideXlab platform.
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does a large number of parameters enhance Model performance comparative assessment of common catchment Model Structures on 429 catchments
Journal of Hydrology, 2001Co-Authors: Charles Perrin, Claude Michel, Vazken AndreassianAbstract:Abstract Hydrological Models must be reliable and robust as these qualities influence all applications based on Model output. Previous studies on conceptual rainfall–runoff Models have shown that one of the root causes of their output uncertainty is Model over-parameterisation. The problem of poorly defined parameters has attracted much attention but has not yet been satisfactorily solved. We believe that the most fruitful way forward is to improve the Structures where these parameters act. The main objective of this paper is to examine the role of complexity in hydrological Models by studying the relation between the number of optimised parameters and Model performance. An extensive comparative performance assessment of the Structures of 19 daily lumped Models was carried out on 429 catchments, mostly in France but also in the United States, Australia, the Ivory Coast and Brazil. Bulk treatment of the data showed that the complex Models outperform the simple ones in calibration mode but not in verification mode. We argue that the main reason why complex Models lack stability is that the structure, i.e. the way components are organised, is not suited to extracting information available in hydrological time-series. An inadequate complexity typically results in Model over-parameterisation and parameter uncertainty. Although complexity has been used as a response to the challenge of predicting the hydrological effects of environmental changes, this study suggests that such Models may have been developed with excessive confidence and that they could face difficulties of parameter estimation and structure validation when confronted with hydro-meteorological time-series. This comparative study indicates that some parsimonious Models can yield promising results and should be further developed, although they are not able to tackle all types of problems, which would be the case if their complexity were ideally adapted.
