934 resultados para Random parameter Logit Model


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The analysis of tourist destination choice, defined by intra-country administrative units and by product types “coastal/inland and village/city”, permits the characterisation of tourist flow behaviour, which is fundamental for public planning and business management. In this study, we analyse the determinant factors of tourist destination choice, proposing various research hypotheses relative to the impact of destination attributes and the personal characteristics of tourists. The methodology applied estimates Nested and Random Coefficients Multinomial Logit Models, which allow control over possible correlations among different destinations. The empirical application is realised in Spain on a sample of 3,781 individuals and allows us to conclude that prices, distance to the destination and personal motivations are determinants in destination choice.

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In the advent of Customer Relationship Management, a more accurate profile of the consumer is needed. The objective of this paper is to show the usefulness of knowing consumer’s complete utility function through his/her marginal utilities. This approach allows one to form groups of individuals with similar preferences (as traditional segmentation methods do) and to treat them individually (which represents an advance). The empirical application is carried out, on a sample of 2,127 individuals, in the context of tourism, where the customer relationship management philosophy is gaining more and more relevance.

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The objective of this study is to test the effect of the consumer’s variety-seeking behaviour on the distance the tourist is prepared to travel; that is, his/her willingness to travel further. The empirical application is carried out in Spain in a context with 26 destinations, by applying Mixed Logit Models. The results evidence that the variety-seeking behaviour reduces the dissuasive effect of distance.

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La literatura de elección de destinos turísticos ha dedicado una gran atención al impacto directo del atributo “precio del destino”, pero no ha alcanzado un consenso en torno al mismo. Alternativamente, nuestro trabajo toma como punto de partida la relación entre las motivaciones turísticas y los beneficios buscados del turista en un destino, lo que lleva a proponer que el efecto del precio viene moderado por las motivaciones del turista a la hora de elegir un destino. Para ello, se argumentan diversas hipótesis de investigación que explican esta decisión a través de la interacción entre dicho atributo del destino y las motivaciones personales de los individuos. La metodología aplicada estima Modelos Logit con Coeficientes Aleatorios que permiten controlar posibles correlaciones entre los distintos destinos y recoger la heterogeneidad de los turistas. La aplicación empírica realizada en España sobre una muestra de 2.127 individuos evidencia que las motivaciones moderan el efecto de los precios en la elección de los destinos turísticos intrapaís.

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The literature contains evidence that there is a marked heterogeneity in price responses to tourism products, leading to a great variety of tourist sensitivities to price. Thus the role price plays is complex, and a particularly challenging aspect of this complexity is that its effect is not unambiguous, thereby negating the idea that the demand for tourism products and tourist activities can always be regarded as demand for ordinary goods. This article identifies and explains, as a novelty for the tourism industry, price sensitivities to tourism activities individual by individual. The operative formalization uses a mixed logit model to estimate the individual sensitivities to price, and then a regression analysis is applied to detect their determinants. The empirical application finds that motivations, influenced by age, and length of stay with a non-linear effect, are explanatory factors of tourists’ price sensitivity to activities.

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Comunicación presentada en CIDUI 2010, Congreso Internacional Docencia Universitaria e Innovación, Barcelona, 30 junio-2 julio 2010.

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This study analyzes the degree of competition through individual actions and reactions. Empirical support for this analysis has derived mainly from structural econometric models describing the nature of competition. This analysis extends the existing literature by empirically considering a direct measurement of competition through the analysis of the competitive actions and responses, and describing how firms compete within and between strategic groups. We estimate the firms’ conduct in the Spanish deposits market with 146 firms and 18,888 observations. This is a specially compelling context for the banking industry, in which a deregulation process gives rise to the adoption of aggressive strategies seeking to increase the market shares of deposit accounts; thus, producing a turbulent situation of increasing rivalry. Our results offer a deeper understanding of the firms’ competitive behavior, since we identify different patterns of actions and reactions depending upon the strategic group the firm belongs to.

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This paper uses the opening of the US textile/apparel market for China at the end of the Multifibre Arrangement in 2005 as a natural experiment to provide evidence for positive assortative matching of Mexican exporting firms and US importing firms by their capability. We identify three findings for liberalized products by comparing them to other textile/apparel products: (1) US importers switched their Mexican partners to those making greater preshock exports, whereas Mexican exporters switched their US partners to those making fewer preshock imports; (2) for firms who switched partners, trade volume of the old partners and the new partners are positively correlated; (3) small Mexican exporters stop exporting. We develop a model combining Becker-type matching of final producers and suppliers with the standard Melitz-type model to show that these findings are consistent with positive assortative matching but not with negative assortative matching or purely random matching. The model indicates that the findings are evidence for a new mechanism of gain from trade.

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Thesis (Master's)--University of Washington, 2016-06

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There has been little study of economic and general attitudes towards the conservation of the Asian elephant. This paper reports and analyses results from surveys conducted in Sri Lanka of attitudes of urban dwellers and farmers towards nature conservation in general and the elephant conservation in particular. The analyses are based on urban and a rural sample. Contingent valuation techniques are used as survey instruments. Multivariate logit regression analysis is used to analyse the respondents' attitudes towards conservation of elephants. It is found that, although some variations occurred between the samples, the majority of the respondents (both rural and urban) have positive attitudes towards nature conservation in general. However, marked differences in attitudes toward elephant conservation are evident between these two samples: the majority of urban respondents were in favour of elephant conservation; rural respondents expressed a mixture of positive and negative attitudes. Overall, considerable unrecorded and as yet unutilised economic support for conservation of wild elephants exists in Sri Lanka. (C) 2002 Elsevier Science Ltd. All rights reserved.

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In this paper, we investigate transmission of electromagnetic wave through aperiodic dielectric multilayers. A generic feature shown is that the mirror symmetry in the system can induce the resonant transmission, which originates from the positional correlations (for example, presence of dimers) in the system. Furthermore, the resonant transmission can be manipulated at a specific wavelength by tuning aperiodic structures with internal symmetry. The theoretical results are experimentally proved in the optical observation of aperiodic SiO2/TiO2 multilayers with internal symmetry. We expect that this feature may have potential applications in optoelectric devices such as the wavelength division multiplexing system.

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The development of a strong, active granular sludge bed is necessary for optimal operation of upflow anaerobic sludge blanket reactors. The microbial and mechanical structure of the granules may have a strong influence on desirable properties such as growth rate, settling velocity and shear strength. Theories have been proposed for granule microbial structure based on the relative kinetics of substrate degradation, but contradict some observations from both modelling and microscopic studies. In this paper, the structures of four granule types were examined from full-scale UASB reactors, treating wastewater from a cannery, a slaughterhouse, and two breweries. Microbial structure was determined using fluorescence in situ hybridisation probing with 16S rRNA-directed oligonucleotide probes, and superficial structure and microbial density (volume occupied by cells and microbial debris) assessed using scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The granules were also modelled using a distributed parameter biofilm model, with a previously published biochemical model structure, biofilm modelling approach, and model parameters. The model results reflected the trophic structures observed, indicating that the structures were possibly determined by kinetics. Of particular interest were results from simulations of the protein grown granules, which were predicted to have slow growth rates, low microbial density, and no trophic layers, the last two of which were reflected by microscopic observations. The primary cause of this structure, as assessed by modelling, was the particulate nature of the wastewater, and the slow rate of particulate hydrolysis, rather than the presence of proteins in the wastewater. Because solids hydrolysis was rate limiting, soluble substrate concentrations were very low (below Monod half saturation concentration), which caused low growth rates. (C) 2003 Elsevier Ltd. All rights reserved.

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Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

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This paper reports preliminary progress on a principled approach to modelling nonstationary phenomena using neural networks. We are concerned with both parameter and model order complexity estimation. The basic methodology assumes a Bayesian foundation. However to allow the construction of pragmatic models, successive approximations have to be made to permit computational tractibility. The lowest order corresponds to the (Extended) Kalman filter approach to parameter estimation which has already been applied to neural networks. We illustrate some of the deficiencies of the existing approaches and discuss our preliminary generalisations, by considering the application to nonstationary time series.

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Increasing mail-survey response using monetary incentives is a proven, but not always cost-effective method in every population. This paper tackles the questions of whether it is worth using monetary incentives and the size of the inducement by testing a logit model of the impact of prepaid monetary incentives on response rates in consumer and organizational mail surveys. The results support their use and show that the inducement value makes a significant impact on the effect size. Importantly, no significant differences were found between consumer and organizational populations. A cost-benefit model is developed to estimate the optimum incentive when attempting to minimize overall survey costs for a given sample size. © 2006 Operational Research Society Ltd. All rights reserved.