Credit Line Analysis plays a very important role in the housing market, especially with the situation of large number of frozen loans during the current financial crisis. In this thesis, we apply the methods of kernel estimate and the Receiver Operating Characteristic (ROC) curve in the credit loan application process in order to help banks select the optimal threshold to differentiate good customers from bad customers. Better choice of the threshold is essential for banks to prevent loss and maximize profit from loans. One of the main advantages of our study is that the method does not require us to specify the distribution of the latent risk score. We apply bootstrap method to construct the confidence interval for the estimate.

In this thesis, two models of marked point processes are investigated. One of the marks have a continuous distribution on a compact Riemannian manifold. The von Mises distribution and its properties are studied. Metropolis-Hastings algorithm of Markov chain Monte Carlo method is used for the simulation of Gibbs segment process. Takacs-Fiksel estimator and its modified version are examined. A kernel density estimator and entropy estimator are proposed and applied to simulated and real data. Powered by TCPDF (www.tcpdf.org)

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第20887号 / 理博第4339号 / 新制||理||1623(附属図書館) / 京都大学大学院理学研究科数学・数理解析専攻 / (主査)准教授 福島 竜輝, 教授 中島 啓, 教授 牧野 和久 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

O padrão espacial de espécies em florestas nativas pode fornecer evidências sobre a estrutura da comunidade vegetal. Fatores ambientais podem influenciar o padrão espacial das espécies, como as características edáficas e processos que dependem da densidade, como competição intra e interespecífica. Desse modo, a pesquisa da relação entre as características ambientais e o padrão espacial de espécies florestais pode ajudar a entender a dinâmica de florestas. O objetivo deste estudo foi empregar técnicas da análise de processos pontuais para verificar o efeito de fatores ambientais sobre a ocorrência de espécies florestais. A área de estudo foi a Estação Ecológica de Assis (EEA), da unidade de Conservação do Estado de São Paulo em parcelas permanentes, dentro do projeto \"Diversidade, dinâmica e conservação em florestas do Estado de São Paulo: 40 ha de parcelas permanentes\" do programa Biota da FAPESP. A descrição do padrão espacial das espécies mais abundantes na área de estudo foi avaliada pela função K proposta por Ripley e suas extensões para processo não homogêneos, por meio das coordenadas geográficas das espécies com circunferência na altura do peito igual ou superior a 15 cm. Modelos do Processo Poisson Homogêneo, Processo Poisson Não Homogêneos e do Processo Log Gaussiano de Cox foram ajustados para cada espécie. Foi utilizado o critério de AIC para selecionar o modelo que melhor se ajusta aos dados. Testes de diagnósticos dos modelos foram feitos utilizando a função K não homogênea sob a hipótese de Completa Aleatoriedade Espacial. Os resultados indicaram que as espécies mais abundantes na EEA apresentam um padrão de distribuição agregado, ou seja, o número esperado de indivíduos próximos de um evento qualquer é maior do que esperado para uma distribuição aleatória. Conforme esperado, os fatores ambientais desempenharam um importante papel para explicar a distribuição espacial das espécies, porém, os resultados indicaram que existe uma variação espacialmente estruturada que não foi incluída na análise que é imprescindível para um bom ajuste dos modelos. Portanto os resultados sugerem que outros fatores não incluídos nos modelos e dados disponíveis podem estar determinando os padrões espaciais além das (co)variáveis medidas. / The spatial pattern of species in native forests may provide evidence on the structure of the plant community. Environmental factors may influence the species\' spatial patterns, as well as soil characteristics and processes which depend on the density, as intraspecific and interspecific competition. Therefore, researching the relationship among the environmental features and the spatial pattern of the forest species may aid in understanding forest dynamics. The goal of this study was to apply point process techniques to verify the effect of environmental factors on the occurence of forest species. The study area was the \"Assis\'s Ecological Station\" (AES), of the \"Unit of conservation of the state of São Paulo in permanent plots\". The data was collected as part of the project entitled \"Diversity, dynamics and conservation in forests of São Paulo state: 40 ha of permanent plots\", from FAPESP\'s Biota program. The description of the spatial pattern of the most abundant species in the study area was assessed using Ripley\'s K function, using the species\' geographic coordinates with circumference at chest height equal or larger than 15 cm. Homogeneous and Non-Homogeneous Poisson Process models, as well as Cox Log Gaussian Process models were fitted to each species. Model selection was made using the Akaike information criterion. Diagnostics tests were made using the non-homogeneous K function under the hypothesis of complete spatial randomness. Results suggested that the most abundant species in the AES present an aggregate distribution pattern, i.e., the expected number of individuals next to any event is larger than the expected by a random distribution. As it was expected, environmental factors played a major role in explaining the spatial distribution of the species. However, results suggested that there is a spatially structured variation that was not included in the analysis and is needed to a good model fit. Therefore, further studies are needed to assess which environmental feature which was not considered in this study presents an effect on the occurence of these forest species

O padrão espacial de espécies em florestas nativas pode fornecer evidências sobre a estrutura da comunidade vegetal. Fatores ambientais podem influenciar o padrão espacial das espécies, como as características edáficas e processos que dependem da densidade, como competição intra e interespecífica. Desse modo, a pesquisa da relação entre as características ambientais e o padrão espacial de espécies florestais pode ajudar a entender a dinâmica de florestas. O objetivo deste estudo foi empregar técnicas da análise de processos pontuais para verificar o efeito de fatores ambientais sobre a ocorrência de espécies florestais. A área de estudo foi a Estação Ecológica de Assis (EEA), da unidade de Conservação do Estado de São Paulo em parcelas permanentes, dentro do projeto \"Diversidade, dinâmica e conservação em florestas do Estado de São Paulo: 40 ha de parcelas permanentes\" do programa Biota da FAPESP. A descrição do padrão espacial das espécies mais abundantes na área de estudo foi avaliada pela função K proposta por Ripley e suas extensões para processo não homogêneos, por meio das coordenadas geográficas das espécies com circunferência na altura do peito igual ou superior a 15 cm. Modelos do Processo Poisson Homogêneo, Processo Poisson Não Homogêneos e do Processo Log Gaussiano de Cox foram ajustados para cada espécie. Foi utilizado o critério de AIC para selecionar o modelo que melhor se ajusta aos dados. Testes de diagnósticos dos modelos foram feitos utilizando a função K não homogênea sob a hipótese de Completa Aleatoriedade Espacial. Os resultados indicaram que as espécies mais abundantes na EEA apresentam um padrão de distribuição agregado, ou seja, o número esperado de indivíduos próximos de um evento qualquer é maior do que esperado para uma distribuição aleatória. Conforme esperado, os fatores ambientais desempenharam um importante papel para explicar a distribuição espacial das espécies, porém, os resultados indicaram que existe uma variação espacialmente estruturada que não foi incluída na análise que é imprescindível para um bom ajuste dos modelos. Portanto os resultados sugerem que outros fatores não incluídos nos modelos e dados disponíveis podem estar determinando os padrões espaciais além das (co)variáveis medidas. / The spatial pattern of species in native forests may provide evidence on the structure of the plant community. Environmental factors may influence the species\' spatial patterns, as well as soil characteristics and processes which depend on the density, as intraspecific and interspecific competition. Therefore, researching the relationship among the environmental features and the spatial pattern of the forest species may aid in understanding forest dynamics. The goal of this study was to apply point process techniques to verify the effect of environmental factors on the occurence of forest species. The study area was the \"Assis\'s Ecological Station\" (AES), of the \"Unit of conservation of the state of São Paulo in permanent plots\". The data was collected as part of the project entitled \"Diversity, dynamics and conservation in forests of São Paulo state: 40 ha of permanent plots\", from FAPESP\'s Biota program. The description of the spatial pattern of the most abundant species in the study area was assessed using Ripley\'s K function, using the species\' geographic coordinates with circumference at chest height equal or larger than 15 cm. Homogeneous and Non-Homogeneous Poisson Process models, as well as Cox Log Gaussian Process models were fitted to each species. Model selection was made using the Akaike information criterion. Diagnostics tests were made using the non-homogeneous K function under the hypothesis of complete spatial randomness. Results suggested that the most abundant species in the AES present an aggregate distribution pattern, i.e., the expected number of individuals next to any event is larger than the expected by a random distribution. As it was expected, environmental factors played a major role in explaining the spatial distribution of the species. However, results suggested that there is a spatially structured variation that was not included in the analysis and is needed to a good model fit. Therefore, further studies are needed to assess which environmental feature which was not considered in this study presents an effect on the occurence of these forest species

Dans ce mémoire de thèse, nous nous intéressons à la modélisation non paramétrique de données spatiales et/ou fonctionnelles, plus particulièrement basée sur la méthode à noyau. En général, les échantillons que nous avons considérés pour établir les propriétés asymptotiques des estimateurs proposés sont constitués de variables dépendantes. La spécificité des méthodes étudiées réside dans le fait que les estimateurs prennent en compte la structure de dépendance des données considérées.Dans une première partie, nous appréhendons l’étude de variables réelles spatialement dépendantes. Nous proposons une nouvelle approche à noyau pour estimer les fonctions de densité de probabilité et de régression spatiales ainsi que le mode. La particularité de cette approche est qu’elle permet de tenir compte à la fois de la proximité entre les observations et de celle entre les sites. Nous étudions les comportements asymptotiques des estimateurs proposés ainsi que leurs applications à des données simulées et réelles.Dans une seconde partie, nous nous intéressons à la modélisation de données à valeurs dans un espace de dimension infinie ou dites "données fonctionnelles". Dans un premier temps, nous adaptons le modèle de régression non paramétrique introduit en première partie au cadre de données fonctionnelles spatialement dépendantes. Nous donnons des résultats asymptotiques ainsi que numériques. Puis, dans un second temps, nous étudions un modèle de régression de séries temporelles dont les variables explicatives sont fonctionnelles et le processus des innovations est autorégressif. Nous proposons une procédure permettant de tenir compte de l’information contenue dans le processus des erreurs. Après avoir étudié le comportement asymptotique de l’estimateur à noyau proposé, nous analysons ses performances sur des données simulées puis réelles.La troisième partie est consacrée aux applications. Tout d’abord, nous présentons des résultats de classification non supervisée de données spatiales (multivariées), simulées et réelles. La méthode de classification considérée est basée sur l’estimation du mode spatial, obtenu à partir de l’estimateur de la fonction de densité spatiale introduit dans le cadre de la première partie de cette thèse. Puis, nous appliquons cette méthode de classification basée sur le mode ainsi que d’autres méthodes de classification non supervisée de la littérature sur des données hydrologiques de nature fonctionnelle. Enfin, cette classification des données hydrologiques nous a amené à appliquer des outils de détection de rupture sur ces données fonctionnelles. / In this dissertation, we are interested in nonparametric modeling of spatial and/or functional data, more specifically based on kernel method. Generally, the samples we have considered for establishing asymptotic properties of the proposed estimators are constituted of dependent variables. The specificity of the studied methods lies in the fact that the estimators take into account the structure of the dependence of the considered data.In a first part, we study real variables spatially dependent. We propose a new kernel approach to estimating spatial probability density of the mode and regression functions. The distinctive feature of this approach is that it allows taking into account both the proximity between observations and that between sites. We study the asymptotic behaviors of the proposed estimates as well as their applications to simulated and real data. In a second part, we are interested in modeling data valued in a space of infinite dimension or so-called "functional data". As a first step, we adapt the nonparametric regression model, introduced in the first part, to spatially functional dependent data framework. We get convergence results as well as numerical results. Then, later, we study time series regression model in which explanatory variables are functional and the innovation process is autoregressive. We propose a procedure which allows us to take into account information contained in the error process. After showing asymptotic behavior of the proposed kernel estimate, we study its performance on simulated and real data.The third part is devoted to applications. First of all, we present unsupervised classificationresults of simulated and real spatial data (multivariate). The considered classification method is based on the estimation of spatial mode, obtained from the spatial density function introduced in the first part of this thesis. Then, we apply this classification method based on the mode as well as other unsupervised classification methods of the literature on hydrological data of functional nature. Lastly, this classification of hydrological data has led us to apply change point detection tools on these functional data.

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.