7 resultados para Discrete choice models
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This work has been presented in: V Conference AERNA, Faro (Portugal), 30 May 2012-1 June 2012 and IV Workshop on Valuation Methods in Agro-food and Environmental Economics, Castelldefels (Barcelona, Spain), 12 July 2012-13 July 2012.
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One of the main problems that public institutions face in the management of protected areas, such as the European Natura 2000 network, is determining how to design and implement sustainable management plans that account for the wide range of marketed and non-marketed benefits they provide to society. This paper presents an application of a stated preference valuation approach aimed at evaluating the social preferences of the population of the Basque Country, Spain, for the key attributes of a regional Natura 2000 network site. According to our results, individuals’ willingness-to-pay (WTP) is higher for attributes associated with non-use values (native tree species and biodiversity conservation) than for attributes associated with use values (agricultural development and commercial forestry). The paper concludes that management policies related to Natura 2000 network sites should account for both for the importance of non-use values and the heterogeneity of the population's preferences in order to minimize potential land use conflicts.
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This paper applies Micken's discretization method to obtain a discrete-time SEIR epidemic model. The positivity of the model along with the existence and stability of equilibrium points is discussed for the discrete-time case. Afterwards, the design of a state observer for this discrete-time SEIR epidemic model is tackled. The analysis of the model along with the observer design is faced in an implicit way instead of obtaining first an explicit formulation of the system which is the novelty of the presented approach. Moreover, some sufficient conditions to ensure the asymptotic stability of the observer are provided in terms of a matrix inequality that can be cast in the form of a LMI. The feasibility of the matrix inequality is proved, while some simulation examples show the operation and usefulness of the observer.
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Published as an article in: Investigaciones Economicas, 2005, vol. 29, issue 3, pages 483-523.
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35 p.
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This paper deals with the convergence of a remote iterative learning control system subject to data dropouts. The system is composed by a set of discrete-time multiple input-multiple output linear models, each one with its corresponding actuator device and its sensor. Each actuator applies the input signals vector to its corresponding model at the sampling instants and the sensor measures the output signals vector. The iterative learning law is processed in a controller located far away of the models so the control signals vector has to be transmitted from the controller to the actuators through transmission channels. Such a law uses the measurements of each model to generate the input vector to be applied to its subsequent model so the measurements of the models have to be transmitted from the sensors to the controller. All transmissions are subject to failures which are described as a binary sequence taking value 1 or 0. A compensation dropout technique is used to replace the lost data in the transmission processes. The convergence to zero of the errors between the output signals vector and a reference one is achieved as the number of models tends to infinity.
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This paper relies on the concept of next generation matrix defined ad hoc for a new proposed extended SEIR model referred to as SI(n)R-model to study its stability. The model includes n successive stages of infectious subpopulations, each one acting at the exposed subpopulation of the next infectious stage in a cascade global disposal where each infectious population acts as the exposed subpopulation of the next infectious stage. The model also has internal delays which characterize the time intervals of the coupling of the susceptible dynamics with the infectious populations of the various cascade infectious stages. Since the susceptible subpopulation is common, and then unique, to all the infectious stages, its coupled dynamic action on each of those stages is modeled with an increasing delay as the infectious stage index increases from 1 to n. The physical interpretation of the model is that the dynamics of the disease exhibits different stages in which the infectivity and the mortality rates vary as the individual numbers go through the process of recovery, each stage with a characteristic average time.