O mercado de Educação Superior privado no Brasil apresenta altos índices de concentração, caracterizando-se como um oligopólio, podendo, portanto, ser estudado sob a ótica da Teoria dos Jogos. Uma das técnicas existentes para abordar este tipo de mercado é conhecida por Análise Conjunta Baseada em Escolhas (Choice Based Conjoint Analysis), que permite estimar as utilidades atribuídas para cada característica dos produtos, prevendo o desejo de cada produto gerado pela combinação dos seus atributos, possibilitando, assim, simular como as decisões de uma amostra de respondentes seriam distribuídas em um mercado simulado entre dois ou mais produtos competidores. Esses modelos, porém, limitam-se a maximizar a receita individual de cada produto, de forma isolada, não levando em conta a possibilidade das firmas terem interesses em maximizar a soma de dois ou mais produtos de forma conjunta. Isso se torna necessário, por exemplo, quando uma empresa comercializa dois produtos que competem no mesmo mercado. Com o objetivo de maximizar a receita conjunta de dois ou mais produtos, foi desenvolvido um método alternativo, baseado em Programação Não-Linar, que foi aplicado em uma cidade brasileira e em um país centro-americano. A comparação dos resultados do modelo desenvolvido com os do modelo tradicional evidencia que o modelo desenvolvido apresenta melhores resultados – soma das receitas das firmas de interesse – gerando uma taxa de crescimento na receita 3% maior, no caso brasileiro e 75% maior no estudo centro-americano. O modelo desenvolvido pode ser adaptado e utilizado em outros mercados oligopolistas ou para otimizar diferentes funções-objetivo. / The Brazilian Higher Education private market shows high levels of concentration and can be considered an oligopoly. Therefore, one can study it as a Game Theory problem. Choice Based Conjoint Analysis – a technic that can be used to approach this kind of market – can estimates the utilities of each products’ features and predict the desire of each product generated by the combination of its attributes. Such technic can simulate how the decisions of a sample of respondents would be distributed among the products of a market made of two or more competitor. These models, however, only maximize the revenues of individual products, not considering the possibility of firms wanting to maximize the sum of the revenue of two or more products. This is useful, for instance, when a company trends two or more products that compete in the same market. An alternative method, based on nonlinear programming, was developed, in order to maximize the conjoint revenue of two or more products and it was applied in a Brazilian city and in a Central American country. Comparing both models – the traditional versus the developed one –, we can see that the developed model shows better outcomes – ie, sum of both companies’ revenues – resulting in a revenue increase rate 3% higher in the Brazilian case and 75% higher in the Central American study. This model can be fitted to other oligopolistic markets or to optimize others objective functions.

This research is the outcome of a preeminent interest in the topic of price perception. Pointedly, the perception of prices is part of the purchasing process, the same willingness to pay and the actual purchase behaviour, and is indubitably a perceptual construct. As such, perception is problematic to measure as it does not relate to an observable behaviour. On the other hand, pricing is regarded as an important variable in the marketing mix. This research contributes to theory by augmenting the current knowledge on the perception of prices including the methods used in the measurement of such perception. Moreover, this research addresses a gap in the understanding of how diverse ethnic groups perceive prices. The relationship set in this study between ethnicity and price perception is thought-provoking as it contributes to the current discussion around diversity in the marketplace. For example, the literature shows advances in areas such as multicultural and ethnic marketing and this research makes a significant contribution to these areas from price perception. Accordingly, this study involved a systematic review of the literature and presented a framework that suggested that the formation of price perception is affected by external factors such as culture and ethnicity. Furthermore, a qualitative study examined the formation of price perception around ethnic groups. Next, this research used a quantitative study that sought differences in price perception among ethnic groups. Thus, the quantitative study used a price perception scale (Lichtenstein et al., 1993) and a choice-based conjoint analysis. Also, the study adopted structural equation modelling (SEM) to measure differences among scales and the multinomial logit model to analyse the choice-based conjoint analysis. The findings of both the quantitative and the qualitative studies link to the systematic review and support the framework for the formation and measurement of price perception originally proposed.

Conjoint analysis is a popular method in consumer preferences research. One of the factors that caused the increasing popularity of this method in recent years is wide use of hierarchical Bayesian models which has been found invaluable in solving the problem of how to obtain reliable estimates of individual preferences without need for overloading respondents with too many conjoint tasks. First goal of my dissertation was to confirm whether the use of Bayesian models is the best choice under all circumstances or whether there are some limitations of this approach. For this purpose I conducted a study based on simulated datasets. Algorithm used enabled generation of datasets that differed in several parameters of interest but which were most comparable in other aspects. Results show that hierarchical models represent choice leading to highest accuracy in predicting respondents' choices in holdout tasks. Use of hierarchical models is most beneficial in the situation of strongly heterogeneous population yet limited amount of available data. In these cases we are able to capture the structure of heterogeneity with significantly lower number of choice task necessary from each respondent. Second goal of the dissertation was to answer the question whether we can increase also the effectiveness of the questioning in conjoint analysis by adding several direct questions. Suggested hybrid choice-based conjoint method (HCBC) combines conjoint analysis tasks with direct questions regarding the preference of levels for each attribute. These are used during the estimation of the model and for increasing the effectiveness if the conjoint analysis tasks design. The HCBC was compared with traditional choice-based conjoint (CBC) and adaptive choice-based conjoint (ACBC) based on practical study involving 421 respondents randomly assigned in one of three test groups. Suggested method has been found as useful alternative that can help with reducing number of choice task needed and as a solution for some situations when diverse importance of the attributes tested does not allow for indirect estimation of preferences with respect to all attributes tested.

Following implementation of the No Child Left Behind Act and Common Core Standards, play experience opportunities by kindergarten students have been compromised. Prior research indicates that how teachers make sense of play is most likely reflected in educational practice. The purpose of this interpretative phenomenological analysis was to gather the lived experiences of 5 kindergarten teachers from northern New England on the nature of play through pre-reflective description and reflective interpretation. Guided by Vygotsky's social constructivist theory as the conceptual framework, the goal of this study was to describe lived play experiences of kindergarten teachers. In-depth, semi-structured interviews were used to answer the main research question about the essence of play as expressed by teachers. Interviews were transcribed, reduced, coded, and analyzed for common thematic elements and essences regarding the impact of how play manifests in curriculum planning and classroom arrangement. Three themes emerged: community building, creative learning, and engaged excitement. The findings revealed that although kindergarten teachers experienced the nature of play differently, play naturally and unequivocally seemed to promote social skills and cooperation, language and concept development, and motivated and self-directed learners. Additional findings showed an incompatibility between the lived world interpretations of kindergarten teachers and the district curriculum expectations. This study influences positive social change by opening educational discussions about kindergarten pedagogy, leading to improved classroom practice.

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Les systèmes de santé accordent une attention croissante au point de vue des usagers dans l’organisation de l’offre de soins. L’instauration d’une offre de soins sensible aux besoins et préférences des patients constitue un enjeu majeur de qualité et d’efficacité des soins. Ce travail analyse le point de vue des patients pour l’offre de soins de médecine générale en utilisant différentes méthodes permettant d’obtenir des informations complémentaires en termes d’expérience de soins, de satisfaction, d’importance ou encore de préférences. Il s’agit des méthodes Delphi, de classement du meilleur au pire et de révélation des préférences par les choix discrets. Ces méthodes sont appliquées sur deux échantillons : en population générale pour la première et chez des patients poly-pathologiques pour les 2 autres afin d’identifier les principaux enjeux actuels et à venir de la réorganisation de l’offre de soins de médecine générale du point de vue des patients. Les résultats montrent le rôle central de la relation médecin-patient et plus particulièrement de l’échange d‘informations entre le médecin et le patient. Cependant une relation médecin-patient de qualité ne doit pas pour autant être réalisée au détriment de la qualité technique du soin et de la coordination de la prise en charge du patient. Ce travail montre également l’importance de prendre en compte l’expérience de soins des patients lors de l’analyse de leur point de vue, et plus particulièrement de leur disposition au changement. L’évaluation systématique et régulière des préférences des patients en pratique quotidienne peut permettre d’améliorer la communication médecin-patient ainsi que le contenu de l’offre de soins du point de vue des patients. / The healthcare systems are paying a great interest to the patients’ perspective for the organization of health care provision. Healthcare system which is accountable and responsive of patients’ needs and preferences is a major issue for the quality and efficiency of care. In this thesis, we analyze the views of patients for the supply of GP care in using different complementary methods about patients’ experience, satisfaction, importance or preferences. These methods are applied to a sample of patients in GP and to a sample of chronically ill patients in order to identify current and future major issues for the reorganization of GP care from the patients’ perspective. The results show the main role of the doctor-patient relationship and especially of the information exchange between doctor and patient and between patient and doctor. However the quality of the doctor-patient relationship is not enough. The technical quality of care (i.e. thoroughness) and the coordination are of high importance for patients. This work highlights that it is necessary to take into account the patients’ experiences in the analysis of their perspective (e.g. preferences) to fully and appropriately understand the results, especially in terms of willingness to change. The systematic and regular screening of patient preferences in daily GP practice can improve the doctor-patient communication and the content of the provision of care from the perspective of patients.

La conception de réseaux est un riche sous-domaine de l'optimisation combinatoire ayant de nombreuses applications pratiques. Du point de vue méthodologique, la plupart des problèmes de cette classe sont notoirement difficiles en raison de leur nature combinatoire et de l'interdépendance des décisions qu'ils impliquent. Ce mémoire aborde deux problèmes de conception de réseaux dont les structures respectives posent des défis bien distincts. Tout d'abord, nous examinons un problème déterministe dans lequel un client doit acquérir au prix minimum un certain nombre d'unités d'un produit auprès d'un ensemble de fournisseurs proposant différents coûts fixes et unitaires, et dont les stocks sont limités. Ensuite, nous étudions un problème probabiliste dans lequel une entreprise entrant sur un marché existant cherche, en ouvrant un certain nombre d'installations parmi un ensemble de sites disponibles, à maximiser sa part espérée d'un marché composé de clients maximisant une fonction d'utilité aléatoire. Ces deux problèmes, soit le problème de transport à coût fixe à un puits et le problème d'emplacement d'installations compétitif basé sur les choix, sont étroitement liés au problème du sac à dos et au problème de couverture maximale, respectivement. Nous introduisons de nouvelles reformulations prenant avantage de ces connexions avec des problèmes classiques d'optimisation combinatoire. Dans les deux cas, nous exploitons ces reformulations pour démontrer de nouvelles propriétés théoriques et développer des méthodes de résolution efficaces. Notre nouvel algorithme pour le problème de transport à coûts fixes à un puits domine les meilleurs algorithmes de la littérature, réduisant le temps de résolution des instances de grande taille jusqu'à quatre ordres de grandeur. Une autre contribution notable de ce mémoire est la démonstration que la fonction objectif du problème d'emplacement d'installations compétitif basé sur les choix est sous-modulaire sous n'importe quel modèle de maximisation d’utilité aléatoire. Notre méthode de résolution basée sur la simulation exploite cette propriété et améliore l'état de l'art pour plusieurs groupes d'instances. / Network design is a rich subfield of combinatorial optimization with wide-ranging real-life applications. From a methodological standpoint, most problems in this class are notoriously difficult due to their combinatorial nature and the interdependence of the decisions they involve. This thesis addresses two network design problems whose respective structures pose very distinct challenges. First, we consider a deterministic problem in which a customer must acquire at the minimum price a number of units of a product from a set of vendors offering different fixed and unit costs and whose supply is limited. Second, we study a probabilistic problem in which a firm entering an existing market seeks, by opening a number of facilities from a set of available locations, to maximize its expected share in a market composed of random utility-maximizing customers. These two problems, namely the single-sink fixed-charge-transportation problem and the choice-based competitive facility location problem, are closely related to the knapsack problem and the maximum covering problem, respectively. We introduce novel model reformulations that leverage these connections to classical combinatorial optimization problems. In both cases, we exploit these reformulations to prove new theoretical properties and to develop efficient solution methods. Our novel algorithm for the single-sink fixed-charge-transportation problem dominates the state-of-the-art methods from the literature, reducing the solving time of large instances by up to four orders of magnitude. Another notable contribution of this thesis is the demonstration that the objective function of the choice-based competitive facility location problem is submodular under any random utility maximization model. Our simulation-based method exploits this property and achieves state-of-the-art results for several groups of instances.

Ce mémoire de thèse porte sur la statistique inférentielle des données spatiales et/ou fonctionnelles. En effet, nous nous sommes intéressés à l’estimation de paramètres inconnus de certains modèles à partir d’échantillons obtenus par un processus d’échantillonnage aléatoire ou non (stratifié), composés de variables indépendantes ou spatialement dépendantes.La spécificité des méthodes proposées réside dans le fait qu’elles tiennent compte de la nature de l’échantillon étudié (échantillon stratifié ou composé de données spatiales dépendantes).Tout d’abord, nous étudions des données à valeurs dans un espace de dimension infinie ou dites ”données fonctionnelles”. Dans un premier temps, nous étudions les modèles de choix binaires fonctionnels dans un contexte d’échantillonnage par stratification endogène (échantillonnage Cas-Témoin ou échantillonnage basé sur le choix). La spécificité de cette étude réside sur le fait que la méthode proposée prend en considération le schéma d’échantillonnage. Nous décrivons une fonction de vraisemblance conditionnelle sous l’échantillonnage considérée et une stratégie de réduction de dimension afin d’introduire une estimation du modèle par vraisemblance conditionnelle. Nous étudions les propriétés asymptotiques des estimateurs proposées ainsi que leurs applications à des données simulées et réelles. Nous nous sommes ensuite intéressés à un modèle linéaire fonctionnel spatial auto-régressif. La particularité du modèle réside dans la nature fonctionnelle de la variable explicative et la structure de la dépendance spatiale des variables de l’échantillon considéré. La procédure d’estimation que nous proposons consiste à réduire la dimension infinie de la variable explicative fonctionnelle et à maximiser une quasi-vraisemblance associée au modèle. Nous établissons la consistance, la normalité asymptotique et les performances numériques des estimateurs proposés.Dans la deuxième partie du mémoire, nous abordons des problèmes de régression et prédiction de variables dépendantes à valeurs réelles. Nous commençons par généraliser la méthode de k-plus proches voisins (k-nearest neighbors; k-NN) afin de prédire un processus spatial en des sites non-observés, en présence de co-variables spatiaux. La spécificité du prédicteur proposé est qu’il tient compte d’une hétérogénéité au niveau de la co-variable utilisée. Nous établissons la convergence presque complète avec vitesse du prédicteur et donnons des résultats numériques à l’aide de données simulées et environnementales.Nous généralisons ensuite le modèle probit partiellement linéaire pour données indépendantes à des données spatiales. Nous utilisons un processus spatial linéaire pour modéliser les perturbations du processus considéré, permettant ainsi plus de flexibilité et d’englober plusieurs types de dépendances spatiales. Nous proposons une approche d’estimation semi paramétrique basée sur une vraisemblance pondérée et la méthode des moments généralisées et en étudions les propriétés asymptotiques et performances numériques. Une étude sur la détection des facteurs de risque de cancer VADS (voies aéro-digestives supérieures)dans la région Nord de France à l’aide de modèles spatiaux à choix binaire termine notre contribution. / This thesis is about statistical inference for spatial and/or functional data. Indeed, weare interested in estimation of unknown parameters of some models from random or nonrandom(stratified) samples composed of independent or spatially dependent variables.The specificity of the proposed methods lies in the fact that they take into considerationthe considered sample nature (stratified or spatial sample).We begin by studying data valued in a space of infinite dimension or so-called ”functionaldata”. First, we study a functional binary choice model explored in a case-controlor choice-based sample design context. The specificity of this study is that the proposedmethod takes into account the sampling scheme. We describe a conditional likelihoodfunction under the sampling distribution and a reduction of dimension strategy to definea feasible conditional maximum likelihood estimator of the model. Asymptotic propertiesof the proposed estimates as well as their application to simulated and real data are given.Secondly, we explore a functional linear autoregressive spatial model whose particularityis on the functional nature of the explanatory variable and the structure of the spatialdependence. The estimation procedure consists of reducing the infinite dimension of thefunctional variable and maximizing a quasi-likelihood function. We establish the consistencyand asymptotic normality of the estimator. The usefulness of the methodology isillustrated via simulations and an application to some real data.In the second part of the thesis, we address some estimation and prediction problemsof real random spatial variables. We start by generalizing the k-nearest neighbors method,namely k-NN, to predict a spatial process at non-observed locations using some covariates.The specificity of the proposed k-NN predictor lies in the fact that it is flexible and allowsa number of heterogeneity in the covariate. We establish the almost complete convergencewith rates of the spatial predictor whose performance is ensured by an application oversimulated and environmental data. In addition, we generalize the partially linear probitmodel of independent data to the spatial case. We use a linear process for disturbancesallowing various spatial dependencies and propose a semiparametric estimation approachbased on weighted likelihood and generalized method of moments methods. We establishthe consistency and asymptotic distribution of the proposed estimators and investigate thefinite sample performance of the estimators on simulated data. We end by an applicationof spatial binary choice models to identify UADT (Upper aerodigestive tract) cancer riskfactors in the north region of France which displays the highest rates of such cancerincidence and mortality of the country.