Detecting Free Riders in Collective Brands through a Hierarchical Choice Process

Free-riding behaviors exist in tourism and they should be analyzed from a comprehensive perspective; while the literature has mainly focused on free riders operating in a destination, the destinations themselves might also free ride when they are under the umbrella of a collective brand. The objective of this article is to detect potential free-riding destinations by estimating the contribution of the different individual destinations to their collective brands, from the point of view of consumer perception. We argue that these individual contributions can be better understood by reflecting the various stages that tourists follow to reach their final decision. A hierarchical choice process is proposed in which the following choices are nested (not independent): “whether to buy,” “what collective brand to buy,” and “what individual brand to buy.” A Mixed Logit model confirms this sequence, which permits estimation of individual contributions and detection of free riders.

Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach

This paper uses an infinite hidden Markov model (IIHMM) to analyze U.S. inflation dynamics with a particular focus on the persistence of inflation. The IHMM is a Bayesian nonparametric approach to modeling structural breaks. It allows for an unknown number of breakpoints and is a flexible and attractive alternative to existing methods. We found a clear structural break during the recent financial crisis. Prior to that, inflation persistence was high and fairly constant.

Establishing erosion susceptibility: analytical hierarchical process and traditional approaches

This study evaluated alternatives for producing erosion susceptibility maps, considering different weight combinations for an environment's attributes, according to four different points of views. The attributes considered were landform, steepness, soils, rocks and land occupation. Considered alternatives were: (1) equal weights, more traditional approach, (2) different weights, according to a previous study in the area, (3) different weights, based on other works in the literature, and (4) different weights based on the analytical hierarchical process. The area studied included the Prosa Basin located in Campo Grande-Mato Grosso do Sul State, Brazil. The results showed that the assessed alternatives can be used together or in different stages of studies aiming at urban planning and decision-making on the interventions to be applied.

Semiparametric Bayesian measurement error modeling

This work presents a Bayesian semiparametric approach for dealing with regression models where the covariate is measured with error. Given that (1) the error normality assumption is very restrictive, and (2) assuming a specific elliptical distribution for errors (Student-t for example), may be somewhat presumptuous; there is need for more flexible methods, in terms of assuming only symmetry of errors (admitting unknown kurtosis). In this sense, the main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in dependent and independent situations. Conditional posterior distributions are implemented, allowing the use of Markov Chain Monte Carlo (MCMC), to generate the posterior distributions. An interesting result shown is that the Dirichlet process prior is not updated in the case of the dependent elliptical model. Furthermore, an analysis of a real data set is reported to illustrate the usefulness of our approach, in dealing with outliers. Finally, semiparametric proposed models and parametric normal model are compared, graphically with the posterior distribution density of the coefficients. (C) 2009 Elsevier Inc. All rights reserved.

A Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem

In this article, we introduce a semi-parametric Bayesian approach based on Dirichlet process priors for the discrete calibration problem in binomial regression models. An interesting topic is the dosimetry problem related to the dose-response model. A hierarchical formulation is provided so that a Markov chain Monte Carlo approach is developed. The methodology is applied to simulated and real data.

Simple approximate MAP inference for Dirichlet processes mixtures

The Dirichlet process mixture model (DPMM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibbs sampling are required. As a result, DPMM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop a simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithm for DPMMs. This algorithm is as simple as DP-means clustering, solves the MAP problem as well as Gibbs sampling, while requiring only a fraction of the computational effort. (For freely available code that implements the MAP-DP algorithm for Gaussian mixtures see http://www.maxlittle.net/.) Unlike related small variance asymptotics (SVA), our method is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables out-of-sample calculations and the use of standard tools such as cross-validation. We illustrate the benefits of our algorithm on a range of examples and contrast it to variational, SVA and sampling approaches from both a computational complexity perspective as well as in terms of clustering performance. We demonstrate the wide applicabiity of our approach by presenting an approximate MAP inference method for the infinite hidden Markov model whose performance contrasts favorably with a recently proposed hybrid SVA approach. Similarly, we show how our algorithm can applied to a semiparametric mixed-effects regression model where the random effects distribution is modelled using an infinite mixture model, as used in longitudinal progression modelling in population health science. Finally, we propose directions for future research on approximate MAP inference in Bayesian nonparametrics.

Bayesian forecasting with highly correlated predictors

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that by acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.

Työpisteiden tiedonkäyttö ja hyödyntäminen osana palveluyhtiön johtamisjärjestelmää

Tutkimuksen tavoitteena oli etsiä kohdeorganisaation taustalla olevia tekijöitä, jotka joko edesauttavat tai estävät nykyisen johtamisjärjestelmän soveltamista, tiedon käyttöä ja hyödyntämistä organisaation työpisteissä. Kohdeorganisaatio on Varenso Oy, Tekniset tuotantopalvelut. Teoriaosiossa käsitellään tietojohtamiseen liittyvää käsitteistöä sekä tiedon luomiseen, johtamiseen ja hyödyntämiseen liittyviä tekijöitä. Johtamista lähestytään myös perustehtävän, strategian ja muutosvalmiuden, valta- ja organisaatiorakenteiden sekä informaatio- ohjauksen näkökulmasta. Lopuksi käsitellään suorituskykyä, tavoitteiden asettamista, mittaamista funktionaalisissa- ja prosessijohdetuissa organisaatioissa. Empiirisessä osiossa tehtiin kyselytutkimus. Tulokset analysoitiin monimuuttujamenetelmiä soveltaen ja löydettiin faktorit, joiden avulla on mahdollista vaikuttaa kohdeorganisaation toimintaan. Kyselytutkimuksen avulla tulkittiin organisaation tämän hetkistä suorituskykyä ja asemaa suhteessa tavoitteisiin. Tuloksena syntyi myös toimenpideehdotuksia.

MODELING HETEROTACHY IN PHYLOGENETICS

Il a été démontré que l’hétérotachie, variation du taux de substitutions au cours du temps et entre les sites, est un phénomène fréquent au sein de données réelles. Échouer à modéliser l’hétérotachie peut potentiellement causer des artéfacts phylogénétiques. Actuellement, plusieurs modèles traitent l’hétérotachie : le modèle à mélange des longueurs de branche (MLB) ainsi que diverses formes du modèle covarion. Dans ce projet, notre but est de trouver un modèle qui prenne efficacement en compte les signaux hétérotaches présents dans les données, et ainsi améliorer l’inférence phylogénétique. Pour parvenir à nos fins, deux études ont été réalisées. Dans la première, nous comparons le modèle MLB avec le modèle covarion et le modèle homogène grâce aux test AIC et BIC, ainsi que par validation croisée. A partir de nos résultats, nous pouvons conclure que le modèle MLB n’est pas nécessaire pour les sites dont les longueurs de branche diffèrent sur l’ensemble de l’arbre, car, dans les données réelles, le signaux hétérotaches qui interfèrent avec l’inférence phylogénétique sont généralement concentrés dans une zone limitée de l’arbre. Dans la seconde étude, nous relaxons l’hypothèse que le modèle covarion est homogène entre les sites, et développons un modèle à mélanges basé sur un processus de Dirichlet. Afin d’évaluer différents modèles hétérogènes, nous définissons plusieurs tests de non-conformité par échantillonnage postérieur prédictif pour étudier divers aspects de l’évolution moléculaire à partir de cartographies stochastiques. Ces tests montrent que le modèle à mélanges covarion utilisé avec une loi gamma est capable de refléter adéquatement les variations de substitutions tant à l’intérieur d’un site qu’entre les sites. Notre recherche permet de décrire de façon détaillée l’hétérotachie dans des données réelles et donne des pistes à suivre pour de futurs modèles hétérotaches. Les tests de non conformité par échantillonnage postérieur prédictif fournissent des outils de diagnostic pour évaluer les modèles en détails. De plus, nos deux études révèlent la non spécificité des modèles hétérogènes et, en conséquence, la présence d’interactions entre différents modèles hétérogènes. Nos études suggèrent fortement que les données contiennent différents caractères hétérogènes qui devraient être pris en compte simultanément dans les analyses phylogénétiques.

Metodologia de disseny conceptual d'estacions depuradores d'aigües residuals que combina el procés de decisió jeràrquic amb l'anàlisi de decisions multicriteri

La present tesi proposa una metodologia de disseny conceptual d'estacions depuradores d'aigües residuals (EDAR) mitjançant la combinació del procés de decisió jeràrquic i l'anàlisi de decisions multicriteri. El document s'inicia amb una breu introducció als principals camps abordats pel treball: el disseny dels processos químics en general, el disseny de les estacions depuradores d'aigües residuals en particular, i l'anàlisi de decisions multicriteri aplicada a la gestió ambiental. Seguidament, es fixen els objectius del treball i es descriuen tant la metodologia com el material de suport informàtic utilitzats. Per validar i contrastar la metodologia de disseny presentada, es desenvolupa un cas d'estudi on es porta a terme el disseny conceptual d'una EDAR que presenta els mateixos requeriments que l'EDAR que opera actualment al municipi de Granollers. Inicialment es presenta la informació de partida i tot seguit es defineixen els objectius de disseny, així com el conjunt de criteris que s'utilitzaran per avaluar en quina mesura es compleixen aquests objectius. Els objectius de disseny són de diferents tipus: ambientals, tècnics, socials i econòmics, i el conjunt de criteris utilitzats, concretament 33, també es classifica segons aquestes quatre categories. Cadascun dels criteris presenta un determinat pes d'importància relativa en la presa de decisions. Finalment, es desenvolupa tot el procés de decisió fins a obtenir el disseny complet de l'EDAR. El procés de decisió s'ha dividit en dues parts diferenciades però que alhora s'entrellacen: la línia d'aigua i la línia de fang. El procés de decisió presenta un total de divuit qüestions amb un màxim de quatre alternatives per pregunta (dotze qüestions corresponen a la línia d'aigua, i sis a la línia de fangs). Per solucionar cadascuna d'aquestes qüestions, s'avaluen les alternatives proposades respecte a un conjunt de criteris triats de la llista inicial. Aplicant el procés de decisió multicriteri anomenat SMART (simple multiattribute rating technique), es combinen els resultats de les alternatives respecte a cada criteri, tenint en compte la importància de cada criteri per obtenir un sol valor per alternativa. Per quantificar els criteris referents a l'operació del procés i les de tipus econòmic s'han utilitzat els programes GPS-X i CapdetWorks respectivament. Pel que fa als criteris no quantificats mitjançant aquests programes, s'han resolt de manera qualitativa i mitjançant manuals de disseny i també tenint en compte l'opinió d'experts en aquest camp. L'alternativa que obté un pes més elevat és la recomanada per al procés de decisió. El cas d'estudi finalitza un cop s'obté el disseny complet de l'EDAR. Per integrar tots aquests elements que hem esmentat i donar suport al desenvolupament del procés de decisió s'ha utilitzat el programa DRAMA (Design Rationale Management). A continuació, es fa una anàlisi comparativa entre l'EDAR que hi ha actualment al municipi de Granollers i l'EDAR resultat del cas d'estudi. Es descriu el diagrama de flux que conforma l'EDAR de Granollers i el diagrama de flux de l'EDAR resultat de l'estudi, se'n fa una anàlisi comparativa justificant cadascuna de les decisions preses en el cas d'estudi i, finalment, es fa una discussió de resultats on es reflecteixen els avantatges associats d'aplicar la metodologia de disseny conceptual proposada. Finalment, es presenten les conclusions de la tesi. Els principals resultats de la tesi es van publicar el 2002 a la revista internacional Industrial and Engineering Chemistry Research (N. Vidal, R. Bañares-Alcántara, I. Rodríguez-Roda i M. Poch: "Design of wastewater treatment plants using a conceptual design methodology", Industrial and Engineering Chemistry Research, 41 (20), pàg. 4993-5005) i la continuació de la línia de recerca al Laboratori d'Enginyeria Química i Ambiental de la UdG ha comportat la presentació del treball de recerca de Xavi Flores "Procés de decisió jeràrquic combinat amb anàlisi multicriteri per al suport al disseny conceptual de sistemes de fangs actius d'una estació depuradora d'aigües residuals" i la presentació dels resultats parcials al congrés internacional de la 9th IWA Conference on Design, Operation and Economics of Large Wastewater Treatment, que va tenir lloc el setembre passat a Praga ("Combining hierarchical decision process with multi-criteria analysis for conceptual design of WWTP", X. Flores, N. Vidal, A. Bonmatí, J. B. Copp i I. Rodríguez-Roda).

On Modular Design of Field Robotic Systems

Robots are needed to perform important field tasks such as hazardous material clean-up, nuclear site inspection, and space exploration. Unfortunately their use is not widespread due to their long development times and high costs. To make them practical, a modular design approach is proposed. Prefabricated modules are rapidly assembled to give a low-cost system for a specific task. This paper described the modular design problem for field robots and the application of a hierarchical selection process to solve this problem. Theoretical analysis and an example case study are presented. The theoretical analysis of the modular design problem revealed the large size of the search space. It showed the advantages of approaching the design on various levels. The hierarchical selection process applies physical rules to reduce the search space to a computationally feasible size and a genetic algorithm performs the final search in a greatly reduced space. This process is based on the observation that simple physically based rules can eliminate large sections of the design space to greatly simplify the search. The design process is applied to a duct inspection task. Five candidate robots were developed. Two of these robots are evaluated using detailed physical simulation. It is shown that the more obvious solution is not able to complete the task, while the non-obvious asymmetric design develop by the process is successful.

Posterior Simulation in the Generalized Linear Model with Semiparmetric Random Effects

Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP) model, normal base measures and Gibbs sampling procedures based on the Pólya urn scheme are often used to simulate posterior draws. These algorithms are applicable in the conjugate case when (for a normal base measure) the likelihood is normal. In the non-conjugate case, the algorithms proposed by MacEachern and Müller (1998) and Neal (2000) are often applied to generate posterior samples. Some common problems associated with simulation algorithms for non-conjugate MDP models include convergence and mixing difficulties. This paper proposes an algorithm based on the Pólya urn scheme that extends the Gibbs sampling algorithms to non-conjugate models with normal base measures and exponential family likelihoods. The algorithm proceeds by making Laplace approximations to the likelihood function, thereby reducing the procedure to that of conjugate normal MDP models. To ensure the validity of the stationary distribution in the non-conjugate case, the proposals are accepted or rejected by a Metropolis-Hastings step. In the special case where the data are normally distributed, the algorithm is identical to the Gibbs sampler.

Bayesian joint modeling of longitudinal and survival data

The joint modeling of longitudinal and survival data is a new approach to many applications such as HIV, cancer vaccine trials and quality of life studies. There are recent developments of the methodologies with respect to each of the components of the joint model as well as statistical processes that link them together. Among these, second order polynomial random effect models and linear mixed effects models are the most commonly used for the longitudinal trajectory function. In this study, we first relax the parametric constraints for polynomial random effect models by using Dirichlet process priors, then three longitudinal markers rather than only one marker are considered in one joint model. Second, we use a linear mixed effect model for the longitudinal process in a joint model analyzing the three markers. In this research these methods were applied to the Primary Biliary Cirrhosis sequential data, which were collected from a clinical trial of primary biliary cirrhosis (PBC) of the liver. This trial was conducted between 1974 and 1984 at the Mayo Clinic. The effects of three longitudinal markers (1) Total Serum Bilirubin, (2) Serum Albumin and (3) Serum Glutamic-Oxaloacetic transaminase (SGOT) on patients' survival were investigated. Proportion of treatment effect will also be studied using the proposed joint modeling approaches. ^ Based on the results, we conclude that the proposed modeling approaches yield better fit to the data and give less biased parameter estimates for these trajectory functions than previous methods. Model fit is also improved after considering three longitudinal markers instead of one marker only. The results from analysis of proportion of treatment effects from these joint models indicate same conclusion as that from the final model of Fleming and Harrington (1991), which is Bilirubin and Albumin together has stronger impact in predicting patients' survival and as a surrogate endpoints for treatment. ^

Modelización de un proceso de elección secuencial en cuatro etapas del consumidor turista

El objetivo de este trabajo consiste en proponer un proceso de decisión secuencial y jerárquico que siguen los turistas vacacionales en cuatro etapas: 1) salir (o no) de vacaciones; 2) elección de un viaje nacional vs. internacional; 3) elección de determinadas áreas geográficas; y 4) elección de la modalidad del viaje -multidestino o de destino fijo- en estas áreas. Este análisis permite examinar las distintas fases que sigue un turista hasta seleccionar una determinada modalidad de viaje en un zona geográfica concreta, así como observar los factores que influyen en cada etapa. La aplicación empírica se realiza sobre una muestra de 3.781 individuos, y estima, mediante procedimientos bayesianos, un Modelo Logit de Coeficientes Aleatorios. Los resultados obtenidos revelan el carácter anidado y no independiente de las decisiones anteriores, lo que confirma el proceso secuencial y jerárquico propuesto.