Orientador: Hildete Prisco Pinheiro / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-10T18:03:10Z (GMT). No. of bitstreams: 1 Maia_RafaelPimentel_M.pdf: 1076568 bytes, checksum: b09cf45158c8c66c120c29b2addafe8c (MD5) Previous issue date: 2008 / Resumo: O objetivo deste trabalho é propor novas metodologias para avaliar o desempenho dos alunos da UNICAMP, do ingresso à conclusão do curso. O conjunto de dados disponível foi obtido a partir dos questionários Sócio-Culturais aplicados pela Comissão Permanente de Vestibulares (COMVEST) na inscrição do vestibular e informações acadêmicas fornecidas pela Diretoria Acadêmica (DAC) da UNICAMP. Estes se referem às informações de todos os alunos ingressantes nos anos de 1997 a 2000. São propostas duas metodologias, uma com base na variável denominada "ganho relativo" sugerido por Dachs e Maia (2006) e a segunda utilizando as notas de todas as disciplinas cursadas pelos alunos durante a graduação. Essas novas metodologias baseiam-se em medidas de diversidades propostas por Rao (1982) e na utilização de U-Estatísticas. São propostos testes de homogeneidade para avaliar se existe diferença no desempenho entre alunos de grupos distintos (alunos oriundos de escola pública ou privada, por exemplo). Aspectos teóricos de U-Estatística e medidas de diversidade também são apresentados. Para a primeira metodologia foram feitas duas abordagens: paramétrica e não paramétrica, enquanto que para a segunda, apenas a abordagem não paramétrica foi explorada. Na abordagem paramétrica as estimativas são feitas por máxima verossimilhança e na não paramétrica foi utilizado o método de re-amostragem por jackknafe para se obter as estimativas das variâncias. Todas as aplicações utilizaram os dados dos alunos ingressantes / Abstract: The main interest of this work is to propose new methods to evaluate the performances of the students at UNICAMP from admission to graduation. The data was obtained from questionnaires applied by the University Commission of admission's exam (COMVEST) during registration of the exam and academic informations provided by the Directory of Academic Studies (DAC). The data refer to information with respect to all the students enrolled in the University from 1997 to 2000. We propose two methods: one based on the variable "relative gain"(Dachs and Maia, 2006) and the other method uses information about the grades of all courses attended by the students during their undergraduate studies. These new methods are based on diversity measures proposed by Rao (1982) and the use of U-Statistics. Homogeneity tests are proposed to evaluate differences in the performance of the students according to different socio-economic groups. For the first method, we have two approaches: a parametric and a nonparametric analysis. For the second method, only a nonparametric analysis was done. In the parametric analysis, a Maximum Likelihood Estimation procedure is used and in the nonparametric analysis, resampling methods such as jackknife was used to obtain the estimates of the variances and confidence intervals. All the applications use the data of the enrolled students / Mestrado / Probabilidade e Estatistica Aplicada / Mestre em Estatística

A principal motivação desta dissertação é um estudo realizado pelo Instituto do Câncer do Estado de São Paulo (ICESP), envolvendo oitocentos e oito pacientes com câncer em estado avançado. Cada paciente foi acompanhado a partir da primeira admissão em uma unidade de terapia intensiva (UTI) pelo motivo de câncer, por um período de no máximo dois anos. O principal objetivo do estudo é avaliar o tempo de sobrevivência e a qualidade de vida desses pacientes através do uso de um tempo ajustado pela qualidade de vida (TAQV). Segundo Gelber et al. (1989), a combinação dessas duas informações, denominada TAQV, induz a um esquema de censura informativa; consequentemente, os métodos tradicionais de análise para dados censurados, tais como o estimador de Kaplan-Meier (Kaplan e Meier, 1958) e o teste de log-rank (Peto e Peto, 1972), tornam-se inapropriados. Visando sanar essa deficiência, Zhao e Tsiatis (1997) e Zhao e Tsiatis (1999) propuseram novos estimadores para a função de sobrevivência e, em Zhao e Tsiatis (2001), foi desenvolvido um teste análogo ao teste log-rank para comparar duas funções de sobrevivência. Todos os métodos considerados levam em conta a ocorrência de censura informativa. Neste trabalho avaliamos criticamente esses métodos, aplicando-os para estimar e testar curvas de sobrevivência associadas ao TAQV no estudo do ICESP. Por fim, utilizamos um método empírico, baseado na técnica de reamostragem bootstrap, a m de propor uma generalização do teste de Zhao e Tsiatis para mais do que dois grupos. / The motivation for this research is related to a study undertaken at the Cancer Institute at São Paulo (ICESP), which comprises the follow up of eight hundred and eight patients with advanced cancer. The patients are followed up from the first admission to the intensive care unit (ICU) for a period up to two years. The main objective is to evaluate the quality-adjusted lifetime (QAL). According to Gelber et al. (1989), the combination of both this information leads to informative censoring; therefore, traditional methods of survival analisys, such as the Kaplan-Meier estimator (Kaplan and Meier, 1958) and log-rank test (Peto and Peto, 1972) become inappropriate. For these reasons, Zhao and Tsiatis (1997) and Zhao and Tsiatis (1999) proposed new estimators for the survival function, and Zhao and Tsiatis (2001) developed a test similar to the log-rank test to compare two survival functions. In this dissertation we critically evaluate and summarize these methods, and employ then in the estimation and hypotheses testing to compare survival curves derived for QAL, the proposed methods to estimate and test survival functions under informative censoring. We also propose a empirical method, based on the bootstrap resampling method, to compare more than two groups, extending the proposed test by Zhao and Tsiatis.

Orientador: Nancy Lopes Garcia / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica / Made available in DSpace on 2018-08-22T12:21:05Z (GMT). No. of bitstreams: 1 Lenzi_Amanda_M.pdf: 1978923 bytes, checksum: 093900c4de8727a9c79d6afdfb0e3bd4 (MD5) Previous issue date: 2013 / Resumo: Neste trabalho tratamos de um problema de dados funcionais agregados, isto é, combinações lineares de curvas aleatórias que não podem ser observadas individualmente. A análise de curvas de carga de transformadores elétricos em linha de distribuição é uma situação onde tais dados são encontrados. O conjunto de dados consiste de diversas curvas agregadas; assumimos que cada curva individual é a realização de um processo Gaussiano com curva média que é modelada através da expansão em bases B-splines. Além disso, assumimos que os coeficientes também são desconhecidos e temos somente uma estimativa de tais números. Nosso objetivo é utilizar os valores aproximados desses coeficientes, além das curvas agregadas, para estimar o valor real dos coeficientes, a curva típica de cada subpopulação e parâmetros de variância. Para este fim, diferentemente de trabalhos anteriores sobre o tema, propomos a utilização de um modelo de efeitos aleatórios / Abstract: In this paper, we deal with a problem of aggregated functional data, i.e. linear combinations of random curves, which cannot be seen individually. The analysis of load curves of electrical transformers in the distribution line is a situation where those data are found. The data set consists of several aggregated curves; we assume that each individual curve is the realization of a Gaussian process with the mean curve modeled through the expansion in B-splines basis. Furthermore, we assume that the coefficients are also unknown and we have only an estimate of such numbers. Our goal is to use the approximate coefficients and the aggregated curve data to estimate the true coefficients, the typical curve of each subpopulation and variance parameters. For this purpose, unlike previous works on the subject, we propose using a random effects model / Mestrado / Estatistica / Mestra em Estatística

M.Sc. (Mathematical Statistics) / Comparison of two distributions via use of the quantile comparison function is carried out specifically from possibly censored data. A semi-parametric method which assumes linearity of the quantile comparison function is examined thoroughly for non-censored data and then extended to incorporate censored data. A fully nonparametric method to construct confidence bands for the quantile comparison function is set out. The performance of all methods examined is tested using Monte Carlo Simulation.

There has been an increasing interest in survival analysis with interval-censored data, where the event of interest (such as infection with a disease) is not observed exactly but only known to happen between two examination times. However, because so much research has been focused on right-censored data, so many statistical tests and techniques are available for right-censoring methods, hence interval-censoring methods are not as abundant as those for right-censored data. In this study, right-censoring methods are used to fit a proportional hazards model to some interval-censored data. Transformation of the interval-censored observations was done using a method called mid-point imputation, a method which assumes that an event occurs at some midpoint of its recorded interval. Results obtained gave conservative regression estimates but a comparison with the conventional methods showed that the estimates were not significantly different. However, the censoring mechanism and interval lengths should be given serious consideration before deciding on using mid-point imputation on interval-censored data.

Dans cette thèse, j'étudie les propriétés théoriques des modèles de Markov cachés non paramétriques. Le choix de modèles non paramétriques permet d'éviter les pertes de performance liées à un mauvais choix de paramétrisation, d'où un récent intérêt dans les applications. Dans une première partie, je m'intéresse à l'estimation du nombre d'états cachés. J'y introduis deux estimateurs consistants : le premier fondé sur un critère des moindres carrés pénalisés, le second sur une méthode spectrale. Une fois l'ordre connu, il est possible d'estimer les autres paramètres. Dans une deuxième partie, je considère deux estimateurs adaptatifs des lois d'émission, c'est-à-dire capables de s'adapter à leur régularité. Contrairement aux méthodes existantes, ces estimateurs s'adaptent à la régularité de chaque loi au lieu de s'adapter seulement à la pire régularité. Dans une troisième partie, je me place dans le cadre mal spécifié, c'est-à-dire lorsque les observations sont générées par une loi qui peut ne pas être un modèle de Markov caché. J'établis un contrôle de l'erreur de prédiction de l'estimateur du maximum de vraisemblance sous des conditions générales d'oubli et de mélange de la vraie loi. Enfin, j'introduis une variante non homogène des modèles de Markov cachés : les modèles de Markov cachés avec tendances, et montre la consistance de l'estimateur du maximum de vraisemblance. / During my PhD, I have been interested in theoretical properties of nonparametric hidden Markov models. Nonparametric models avoid the loss of performance coming from an inappropriate choice of parametrization, hence a recent interest in applications. In a first part, I have been interested in estimating the number of hidden states. I introduce two consistent estimators: the first one is based on a penalized least squares criterion, and the second one on a spectral method. Once the order is known, it is possible to estimate the other parameters. In a second part, I consider two adaptive estimators of the emission distributions. Adaptivity means that their rate of convergence adapts to the regularity of the target distribution. Contrary to existing methods, these estimators adapt to the regularity of each distribution instead of only the worst regularity. The third part is focussed on the misspecified setting, that is when the observations may not come from a hidden Markov model. I control of the prediction error of the maximum likelihood estimator when the true distribution satisfies general forgetting and mixing assumptions. Finally, I introduce a nonhomogeneous variant of hidden Markov models : hidden Markov models with trends, and show that the maximum likelihood estimators of such models is consistent.

An ever-increasing number of functional magnetic resonance imaging (fMRI) studies are now using information-based multi-voxel pattern analysis (MVPA) techniques to decode mental states. In doing so, they achieve a significantly greater sensitivity compared to when they use univariate analysis frameworks. Two most prominent MVPA methods for information mapping are searchlight decoding and classifier weight mapping. The new MVPA brain mapping methods, however, have also posed new challenges for analysis and statistical inference on the group level. In this thesis, I discuss why the usual procedure of performing t-tests on MVPA derived information maps across subjects in order to produce a group statistic is inappropriate. I propose a fully nonparametric solution to this problem, which achieves higher sensitivity than the most commonly used t-based procedure. The proposed method is based on resampling methods and preserves the spatial dependencies in the MVPA-derived information maps. This enables to incorporate a cluster size control for the multiple testing problem. Using a volumetric searchlight decoding procedure and classifier weight maps, I demonstrate the validity and sensitivity of the new approach using both simulated and real fMRI data sets. In comparison to the standard t-test procedure implemented in SPM8, the new results showed a higher sensitivity and spatial specificity. The second goal of this thesis is the comparison of the two widely used information mapping approaches -- the searchlight technique and classifier weight mapping. Both methods take into account the spatially distributed patterns of activation in order to predict stimulus conditions, however the searchlight method solely operates on the local scale. The searchlight decoding technique has furthermore been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. In this thesis, I compare searchlight decoding with linear classifier weight mapping, both using the formerly proposed non-parametric statistical framework using a simulation and ultra-high-field 7T experimental data. It was found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, the weight mapping method was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, such global multivariate methods provide a substantial improvement for characterizing structure-function relationships.

Indiana University-Purdue University Indianapolis (IUPUI) / Randomized studies are designed to estimate the average treatment effect (ATE) of an intervention. Individuals may derive quantitatively, or even qualitatively, different effects from the ATE, which is called the heterogeneity of treatment effect. It is important to detect the existence of heterogeneity in the treatment responses, and identify the different sub-populations. Two corresponding statistical methods will be discussed in this talk: a hypothesis testing procedure and a mixture-model based approach. The hypothesis testing procedure was constructed to test for the existence of a treatment effect in sub-populations. The test is nonparametric, and can be applied to all types of outcome measures. A key innovation of this test is to build stochastic search into the test statistic to detect signals that may not be linearly related to the multiple covariates. Simulations were performed to compare the proposed test with existing methods. Power calculation strategy was also developed for the proposed test at the design stage. The mixture-model based approach was developed to identify and study the sub-populations with different treatment effects from an intervention. A latent binary variable was used to indicate whether or not a subject was in a sub-population with average treatment benefit. The mixture-model combines a logistic formulation of the latent variable with proportional hazards models. The parameters in the mixture-model were estimated by the EM algorithm. The properties of the estimators were then studied by the simulations. Finally, all above methods were applied to a real randomized study in a low ejection fraction population that compared the Implantable Cardioverter Defibrillator (ICD) with conventional medical therapy in reducing total mortality.

A 2009 report published by the National Research Council addressed the need for improvements in the field of forensic science. In the report emphasis was placed on the need for more rigorous scientific analysis within many forensic science disciplines and for established limitations and determination of error rates from statistical analysis. This research focused on multivariate statistical techniques for the analysis of spectral data obtained for multiple forensic applications which include samples from: automobile float glasses and paints, bones, metal transfers, ignitable liquids and fire debris, and organic compounds including explosives. The statistical techniques were used for two types of data analysis: classification and discrimination. Statistical methods including linear discriminant analysis and a novel soft classification method were used to provide classification of forensic samples based on a compiled library. The novel soft classification method combined three statistical steps: Principal Component Analysis (PCA), Target Factor Analysis (TFA), and Bayesian Decision Theory (BDT) to provide classification based on posterior probabilities of class membership. The posterior probabilities provide a statistical probability of classification which can aid a forensic analyst in reaching a conclusion. The second analytical approach applied nonparametric methods to provide the means for discrimination between samples. Nonparametric methods are performed as hypothesis test and do not assume normal distribution of the analytical figures of merit. The nonparametric iv permutation test was applied to forensic applications to determine the similarity between two samples and provide discrimination rates. Both the classification method and discrimination method were applied to data acquired from multiple instrumental methods. The instrumental methods included: Laser Induced-Breakdown Spectroscopy (LIBS), Fourier Transform Infrared Spectroscopy (FTIR), Raman spectroscopy, and Gas Chromatography-Mass Spectrometry (GC-MS). Some of these instrumental methods are currently applied to forensic applications, such as GC-MS for the analysis of ignitable liquid and fire debris samples; while others provide new instrumental methods to areas within forensic science which currently lack instrumental analysis techniques, such as LIBS for the analysis of metal transfers. The combination of the instrumental techniques and multivariate statistical techniques is investigated in new approaches to forensic applications in this research to assist in improving the field of forensic science.

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