Made available in DSpace on 2015-03-26T13:32:04Z (GMT). No. of bitstreams: 1 texto completo.pdf: 569181 bytes, checksum: 1b525772884dca74fcef6c9c8033aaa5 (MD5) Previous issue date: 2008-09-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Reburning caused by Phytophthora infestansis is characterized as an aggressive disease of great destructive impact, capable of limiting or even hindering the economic cultivation of the tomato plant under conditions of high humidity and low temperatures. In view of the problems reburning can cause to tomato plant crops, this work aimed to: 1) fit models to describe the progress of the disease and form groups of tomato accesses with similar curves; 2) estimate data referring to the number of days to reach 5% severity of the disease, by means of inverse regression; 3) fit survival curves by means of the Kaplan-Meier estimator for the access groups and compare them by means of the Logrank test;4)fit survival curves by means of probabilistic models and compare these curves with Kaplan Meir´s non-parametric technique. Using tomato reburning real data, it was possible to fit the exponential model (Y = y0 exp (rX)) to describe the disease s progress. The means of the parameter estimates were submitted to grouping analysis using the centroid method, generating 10 access groups. Time up to 5% of the disease was calculated via inverse regression. Non-parametric techniques were used to estimate survival function by means of the Kaplan-Meier´s estimator to compare the survival curves by the Logrank test .The survival function was also fit using the probabilistic models, exponential Weibull and Log-normal, respectively, which were compared by means of the verisimilitude ratio test (VRT), considering the generalized Gamma model, as a general case for these models. The methodology applied allowed fitting the exponential model to describe tomato plant reburning progress and to regroup the accesses studied in the 10 groups. The access BGH-6 obtained a smaller disease progress than the others, thus characterizing its higher resistance to the disease; An inverse regression allowed time estimation up to the occurrence of 5% of the severity of the tomato plant reburning. The Kaplan-Meier ´s non-parametric technique allowed estimating the survival curves of the tomato plant accesses belonging to the groups 1, 2, 4, 6 and 8. Utilizing the Logrank test, it could be concluded that most two-by-two comparisons were significant (p<0.05), except in the comparisons of groups 2x4, 4x8 and 6x8. The use of the probabilistic models, exponential Weibull and Log-normal allowed estimating the survival curves of groups 2, 4, 6 and 8, except for group 4, to which the Weibull model was not adequate. Comparing the probabilistic models with the non-parametric technique, the curves of the probabilistic models of groups 2 and 4 presented satisfactory results, compared to the curve estimated by Kaplan-Meier. / A requeima causada por Phytophthora infestans caracteriza-se por ser uma doença agressiva e de grande impacto destrutivo, podendo limitar ou até mesmo impedir o cultivo econômico do tomateiro sob condições de alta umidade e baixas temperaturas. Diante dos problemas que a requeima pode provocar às lavouras de tomate, este trabalho teve por objetivos: 1) ajustar modelos para descrever o progresso da doença e formar grupos de acessos de tomateiro com curvas semelhantes; 2) estimar dados referentes ao número de dias até atingir 5% de severidade da doença, por meio de regressão inversa; 3) ajustar curvas de sobrevivência por meio do estimador de Kaplan-Meier para grupos de acessos e compará-las mediante o uso do teste Logrank; 4) ajustar curvas de sobrevivência por meio de modelos probabilísticos e compará-las com a técnica não-paramétrica de Kaplan-Meier. Utilizando dados reais sobre a requeima do tomateiro, foi possível ajustar o modelo exponencial (Y = y0 exp (rX)) para descrever o progresso da doença. As médias das estimativas dos parâmetros foram submetidas à análise de agrupamento pelo método Centróide, o que gerou 10 grupos de acessos, sendo o tempo até a incidência de 5% da doença calculado via regressão inversa. Foram utilizadas técnicas não-paramétricas para estimar a função de sobrevivência por meio do estimador de Kaplan-Meier e para comparar as curvas de sobrevivência pelo teste Logrank. Foi também ajustada a função de sobrevivência, empregando-se os modelos probabilísticos Exponencial, Weibull e Log-normal, os quais foram comparados por meio do Teste da Razão da Verossimilhança (TRV), considerando-se o modelo Gama generalizado por ser caso geral para esses modelos. A metodologia utilizada permitiu ajustar o modelo Exponencial para descrever o progresso da requeima do tomateiro e agrupar os acessos estudados em 10 grupos. O acesso BGH-6 sofreu um progresso de doença menor que os demais, caracterizando-se, assim, sua maior resistência à enfermidade. A regressão inversa possibilitou estimar o tempo até a ocorrência de 5% da severidade da requeima do tomateiro. Pela técnica não-paramétrica de Kaplan-Meier, foi possível estimar as curvas de sobrevivência dos acessos de tomateiro pertencentes aos grupos 1, 2, 4, 6 e 8. Utilizando o teste Logrank, pode-se concluir que a maioria das comparações duas a duas foi significativa (p<0,05), exceto nas comparações dos grupos 2x4, 4x8 e 6x8. O uso dos modelos probabilísticos Exponencial, Weibull e Log-normal possibilitou a estimação das curvas de sobrevivência nos grupos 2, 4, 6 e 8, exceto no grupo 4, em que o modelo Weibull não foi adequado. Comparando os modelos probabilísticos com a técnica não-paramétrica, as curvas dos modelos probabilísticos dos grupos 2 e 4 apresentaram ajustes satisfatórios com relação à curva estimada por Kaplan-Meier.

Submitted by Luis Guilherme Macena (guilhermelg2004@gmail.com) on 2013-04-08T16:55:05Z No. of bitstreams: 1 Laurinda Yoko Shinzato Higa_TESE.pdf: 1486069 bytes, checksum: f70375f751ecd6d4742cf70048bbdb2e (MD5) / Made available in DSpace on 2013-04-08T16:55:05Z (GMT). No. of bitstreams: 1 Laurinda Yoko Shinzato Higa_TESE.pdf: 1486069 bytes, checksum: f70375f751ecd6d4742cf70048bbdb2e (MD5) Previous issue date: 2011 / Fundação Oswaldo Cruz. Instituto Fernandes Figueira. Departamento de Ensino. Programa de Pós-Graduação em Saúde da Criança e da Mulher. Rio de Janeiro, RJ, Brasil / Introdução: A fibrose cística (FC) é uma doença genética, de transmissão autossômica recessiva, que compromete múltiplos órgãos, que apresenta curso crônico e progressivo sendo considerada potencialmente letal. Objetivos: Estimar a sobrevivência dos pacientes com FC e os fatores de risco associados à redução no tempo de sobrevivência. Métodos: Tratou-se de uma coorte aberta de casos diagnosticados entre 01/01/1990 e 10/10/2009 no Centro de Referência em FC do RJ, CRFC-RJ, Brasil na qual se analisou a sobrevivência global e fatores de risco associados com a sobrevida dos pacientes. O período de risco iniciou-se na idade ao diagnóstico e terminou na idade quando ocorreu o óbito por FC, a perda de seguimento ou o fim do estudo. Os fatores analisados foram: sexo, motivo da suspeita diagnóstica, genótipo, número de órgãos comprometidos, estado nutricional, colonização bacteriana, reposição enzimática e década do diagnóstico. As curvas de sobrevivência foram estimadas pelo método Kaplan- Meier, ajustadas para truncamento à esquerda e para dados censurados à direita. A seguir, as hazard ratios (HR) foram estimadas pelo modelo de Cox, utilizando o processo de contagem, tendo a idade como escala de tempo e avaliadas pelo teste de razão de verossimilhança, e os modelos comparados pela análise de resíduos. Resultados: A população (n=177) apresentou o predomínio do sexo feminino (56%) e a idade mediana ao diagnóstico foi 14 meses. A idade mediana de sobrevivência foi 20,8 anos. Após o diagnóstico 81% sobreviveram até cinco anos; 70% até 10 anos e 61% até 14,5 anos. O modelo explicou 19,9% dos efeitos e incluiu seis covariáveis: colonização por Pseudomonas aeruginosa, isolada e associada (HR = 10,30; IC95% = 2,41-43,97), por Staphylococcus aureus (HR=4,50; IC95% = 0,93-1,85), por outras bactérias (HR=3,38; IC95% = 0,92-1,32), sexo feminino (HR=1,95; IC95% = 0,96-3,96), estado nutricional ≤ p5 (HR=1,94; IC95% = 0,94-3,98) e diagnóstico na década de 1990 (HR=4,34; IC95% = 1,50-12,52). Conclusão: Este estudo de coorte de 177 pacientes com FC mostrou uma idade mediana de sobrevivência de 20,8 anos dos pacientes no CRFC-RJ. Foram confirmados os efeitos das covariáveis que, presentes ao diagnóstico, se associaram a maior mortalidade. A intervenção nestas covariáveis promoverá a recuperação nutricional, a erradicação da Pseudomonas aeruginosa ou o adiamento da colonização crônica, dessa forma aumentando a sobrevivência. / Introduction: Cystic Fibrosis (CF) is a rare genetic disease, of autossomal recessive transmission, with multiple organ involvement, progressive course and potentially lethal. Objective: To study the CF patients survival and to find the factors associated with. Methods: In an open cohort of cases diagnosed between 01/01/1990 and 10/10/2009 in a CF reference center in Rio de Janeiro, we analyzed global survival and risk factors associated with the survival of CF patients. The at-risk period started at the age of CF diagnosis and ended at age of death, loss of follow-up or end of the study. The factors analyzed were gender, presentation mode, genotype, number of involved organs, nutritional state, bacterial colonization, enzyme replacement and decade of diagnosis. Survival curves were estimated by Kaplan-Meier (KM) adjusted for left truncation and right censored data. Hazard ratios (HR) were estimated by Cox model using counting process approach with age as time scale and evaluated by likelihood ratio test. Model diagnostic was conducted by residuals analysis. Results: The majority of the population (n=177) was female (56%) and during the study the median age at diagnosis was 14 months. The median survival was of 20.8 years. After diagnosis, 81% survived up to 5 years, 70% up to 10 and 61% up to 14.5. The model explained 19.9% of the effects and included six covariates: Pseudomonas aeruginosa colonization, isolated or associated (HR = 10.30; 95%CI = 2.41-43.97), for Staphylococcus aureus (HR = 4.50; CI95% = 0.93-1.85), for other bacteria (HR = 3.38; CI95% = 0.92-1.32), for female gender (HR = 1.95; CI95% = 0.96-3.96), for nutritional state ≤ p5 (HR = 1.94; CI95% = 0.94-3.98), and for diagnostic decade (HR = 4.34; CI95% = 1.50-12.52). Conclusion: The strength of risk factors found at diagnosis was evident in the prognosis besides indicating that interventions may reduce morbidity by nutritional recovery and by Pseudomonas aeruginosa eradication thus increasing survival.

Cette thèse porte sur l'étude des propriétés asymptotiques d'un estimateur non paramétrique de la densité de type Parzen-Rosenblatt, sous un modèle de données censurées à droite, vérifiant une structure de dépendance de type associé. Dans ce cadre, nous rappelons d'abord les résultats existants, avec détails, dans les cas i.i.d. et fortement mélangeant (α-mélange). Sous des conditions de régularité classiques, il est établi que la vitesse de coonvergence uniforme presque sûre de l'estimateur étudié, est optimale. Dans la partie dédiée aux résultats de cette thèse, deux résultats principaux et originaux sont présentés : le premier résultat concerne la convergence uniforme presque sûre de l'estimateur étudié sous l'hypothèse d'association. L'outil principal ayant permis l'obtention de la vitesse optimale est l'adaptation du Théorème de Doukhan et Neumann (2007), dans l'étude du terme des fluctuations (partie aléatoire) de l'écart entre l'estimateur considéré et le paramètre étudié (densité). Comme application, la convergence presque sûre de l'estimateur non paramétrique du mode est établie. Les résultats obtenus ont fait l'objet d'un article accepté pour publication dans Communications in Statistics-Theory and Methods ; Le deuxième résultat établit la normalité asymptotique de l'estimateur étudié sous le même modèle et constitute ainsi une extension au cas censuré, du résultat obtenu par Roussas (2000). Ce résultat est soumis pour publication. / This thesis deals with the study of asymptotic properties of e kernel (Parzen-Rosenblatt) density estimate under associated and censored model. In this setting, we first recall with details the existing results, studied in both i.i.d. and strong mixing condition (α-mixing) cases. Under mild standard conditions, it is established that the strong uniform almost sure convergence rate, is optimal. In the part dedicated to the results of this thesis, two main and original stated results are presented : the first result concerns the strong uniform consistency rate of the studied estimator under association hypothesis. The main tool having permitted to achieve the optimal speed, is the adaptation of the Theorem due to Doukhan and Neumann (2007), in studying the term of fluctuations (random part) of the gap between the considered estimator and the studied parameter (density). As an application, the almost sure convergence of the kernel mode estimator is established. The stated results have been accepted for publication in Communications in Statistics-Theory & Methods ; The second result establishes the asymptotic normality of the estimator studied under the same model and then, constitute an extension to the censored case, the result stated by Roussas (2000). This result is submitted for publication.

Dans cette thèse, nous étudions les propriétés asymptotiques de paramètres fonctionnels conditionnels en statistique non paramétrique, quand la variable explicative prend ses valeurs dans un espace de dimension infinie. Dans ce cadre non paramétrique, on considère les estimateurs des paramètres fonctionnels usuels, tels la loi conditionnelle, la densité de probabilité conditionnelle, ainsi que le quantile conditionnel. Le premier travail consiste à proposer un estimateur du quantile conditionnel et de prouver sa convergence uniforme sur un sous-ensemble compact. Afin de suivre la convention dans les études biomédicales, nous considérons une suite de v.a {Ti, i ≥ 1} identiquement distribuées, de densité f, censurée à droite par une suite aléatoire {Ci, i ≥ 1} supposée aussi indépendante, identiquement distribuée et indépendante de {Ti, i ≥ 1}. Notre étude porte sur des données fortement mélangeantes et X la covariable prend des valeurs dans un espace à dimension infinie.Le second travail consiste à établir la normalité asymptotique de l’estimateur à noyau du quantile conditionnel convenablement normalisé, pour des données fortement mélangeantes, et repose sur la probabilité de petites boules. Plusieurs applications à des cas particuliers ont été traitées. Enfin, nos résultats sont appliqués à des données simulées et montrent la qualité de notre estimateur. / In this thesis, we study some asymptotic properties of conditional functional parameters in nonparametric statistics setting, when the explanatory variable takes its values in infinite dimension space. In this nonparametric setting, we consider the estimators of the usual functional parameters, as the conditional law, the conditional probability density, the conditional quantile. We are essentially interested in the problem of forecasting in the nonparametric conditional models, when the data are functional random variables. Firstly, we propose an estimator of the conditional quantile and we establish its uniform strong convergence with rates over a compact subset. To follow the convention in biomedical studies, we consider an identically distributed sequence {Ti, i ≥ 1}, here density f, right censored by a random {Ci, i ≥ 1} also assumed independent identically distributed and independent of {Ti, i ≥ 1}. Our study focuses on dependent data and the covariate X takes values in an infinite space dimension. In a second step we establish the asymptotic normality of the kernel estimator of the conditional quantile, under α-mixing assumption and on the concentration properties on small balls of the probability measure of the functional regressors. Many applications in some particular cases have been also given.

The modern electronic financial market is composed of a large amount of actors. With the surge in algorithmic trading some of these actors collectively behave in increasingly complex ways. Historically, academic research related to financial markets has been focused on areas such as asset pricing, portfolio management and financial econometrics. However, the fragmentation of the financial market has given rise to a different set of problems, namely the order allocation problem, as well as smart order routers as a tool to comply with these. In this thesis we consider price discrepancies between order books, trading the same instruments, as a proxy for order routing opportunities. A survival analysis framework for these price differences is developed. Specifically, we consider the two widely used Kaplan-Meier and Cox Proportional Hazards models, as well as the somewhat less known Random Survival Forest model, in order to investigate whether such a framework is effective for predicting the survival times of price differences. The results show that the survival models outperform random models and fixed routing decisions significantly. Thus suggesting that such models could beneficially be incorporated into existing SOR environments. Furthermore, the implementation of order book parameters as covariates in the CPH and RSF models add additional performance. / Den moderna elektroniska marknaden består av ett stort antal aktörer som, till följd av ökningen av algoritmisk handel, beter sig alltmer komplext. Historiskt sett har akademisk forskning inom finans i huvudsak fokuserat på områden som prissättning av tillgångar, portföljförvaltning och finansiell ekonometri. Fragmentering av finansiella marknader har däremot gett upphov till nya sorters problem, däribland orderplaceringsproblemet. Följdaktligen har smart order routers utvecklats som ett verktyg för att tillmötesgå detta problem. I detta examensarbete studerar vi prisskillnader mellan orderböcker som tillhandhåller handel av samma instrument. Dessa prisskillnader representerar möjligheter för order routing. Vi utvecklar ett ramverk inom överlevnadsanalys för dessa prisskillnader. Specifikt används de välkända Kaplan-Meier- och Cox Proportional Hazards-modellerna samt den något mindre kända Random Survival Forest, för att utvärdera om ett sådant ramverk kan användas för att förutspå prisskillnadernas livstider. Våra resultat visar att dessa modeller överträffar slumpmässiga modeller samt deterministiska routingstrategier med stor marginal och antyder därmed att ett sådant ramverk kan integreras i SOR-system. Resultaten visar dessutom att användning av orderboksparametrar som variabler i CPH- och RSF-modellerna ökar prestandan.

Master of Science / Department of Statistics / Paul I. Nelson / Quantiles are useful in describing distributions of component lifetimes. Data, consisting of the lifetimes of sample units, used to estimate quantiles are often censored. Right censoring, the setting investigated here, occurs, for example, when some test units may still be functioning when the experiment is terminated. This study investigated and compared the performance of parametric and nonparametric estimators of quantiles from right censored data generated from Weibull and Lognormal distributions, models which are commonly used in analyzing lifetime data. Parametric quantile estimators based on these assumed models were compared via simulation to each other and to quantile estimators obtained from the nonparametric Kaplan- Meier Estimator of the survival function. Various combinations of quantiles, censoring proportion, sample size, and distributions were considered. Our simulation show that the larger the sample size and the lower the censoring rate the better the performance of the estimates of the 5th percentile of Weibull data. The lognormal data are very sensitive to the censoring rate and we observed that for higher censoring rates the incorrect parametric estimates perform the best. If you do not know the underlying distribution of the data, it is risky to use parametric estimates of quantiles close to one. A limitation in using the nonparametric estimator of large quantiles is their instability when the censoring rate is high and the largest observations are censored. Key Words: Quantiles, Right Censoring, Kaplan-Meier estimator

In this thesis, the reliability of small wind turbines is studied. Both conventional reliability analysis methods and the novel Bayesian models (Bayesian Hierarchical Modelling (BHM)) are used to analyse the reliability performance of the Gaia-Wind turbines / assemblies and components of the Gaia-Wind turbine. In Chapter 2, a simple failure mode and effect analysis (FMEA) is conducted. An approximated risk priority number (RPN) is calculated for each failure mode and assembly. The assembly that is identified to have the highest RPN is the "Rotor and Blade Assembly". As for the failure modes, "Blade Split" and "Generator Failure" failure modes are identified to have the highest RPNs. In Chapter 3, the conventional methods including the Kaplan-Meier Analysis, Weibull Plot Analysis, Homogeneous Poisson Process (HPP) Analysis, and Crow-AMSAA (Non-Homogeneous Poisson Process (NHPP)) Analysis are used to study the reliability performance of the generic turbine and the critical assemblies based on the approximated RPNs. By using these conventional methods, the L10 life can be approximated (Kaplan-Meier), the main failure modes of an assembly can be identified (Weibull Plot Analysis), the annual failure rate can be estimated (HPP), and the number of future failures can be predicted (NHPP). These methods have been implemented in a novel on-line interactive platform, named ReliaOS (Chapter 7), which effectively facilitates the process of converting the information in the warranty record to the meaningful reliability information. Three novel BHM models are proposed and implemented in WinBUGS (an open source software), namely the repair model, the environmental model, and the informative prior framework, (Chapter 5 and Chapter 6). The repair model is used to quantify the repair effectiveness of a generic repair action. The model is applied on both the turbine level as well as the component level. At the turbine level, the annual failure rate of the generic turbine is predicted to be 0:159 per turbine per year at the first year. Individual turbines can be categorised into different quality levels ("Good", "Good- Normal", "Normal", "Normal-Bad", and "Bad") based on the predicted annual failure rate values. At the component level, "Blade split", "Cracked Frame", and "Generator Failure" failure modes are studied. These are the most critical failure modes for "Rotor and Blade Assembly", "Tower, Foundation, and Nacelle", and "Generator" assemblies respectively. "Cracked Frame" failure mode is predicted to have the lowest characteristic life and a slightly increasing failure rate trend. The repair effectiveness of the "Cracked Frame" failure mode is identified to be slightly ineffective. The environmental model quantifies the influence of three environmental covariates, i.e. AverageWind Speed (AWS), Turbulence Intensity (TI), and Terrain Slope (TS). These environmental covariates are all identified to have negative impact to the reliability of the generic turbine, where TI and AWS have more pronounced impact than TS. The informative prior BHM framework offers a way of quantifying the reliability of the drivetrain frame (which corresponds to the "Cracked Frame" failure mode) in a situation where zero failure instance is recorded for the new drivetrain frame design. This is achieved by jointly considering the simulation results from SOLIDWORKS as the prior information into the BHM model. This thesis strives to understand the reliability performance of the Gaia-Wind small wind turbine from different perspectives, i.e. the generic turbine, individual turbines, and the components, by the use of conventional methods and the proposed BHM models. The novel on-line reliability platform, ReliaOS, mitigates the difficulties in converting the information in the data to the reliability information for the end users. It is believed that the proposed BHM models and the ReliaOS on-line reliability analysis platform will improve the reliability analysis of small-wind turbines.

A frequent problem that appears in practical survival data analysis is censoring. A censored observation occurs when the observation of the event time (duration or survival time) may be prevented by the occurrence of an earlier competing event (censoring time). Censoring may be due to different causes. For example, the loss of some subjects under study, the end of the follow-up period, drop out or the termination of the study and the limitation in the sensitivity of a measurement instrument. The literature about censored data focuses on the i.i.d. case. However in many real applications the data are collected sequentially in time or space and so the assumption of independence in such case does not hold. Here we only give some typical examples from the literature involving correlated data which are subject to censoring. In the clinical trials domain it frequently happens that the patients from the same hospital have correlated survival times due to unmeasured variables like the quality of the hospital equipment. Censored correlated data are also a common problem in the domain of environmental and spatial (geographical or ecological) statistics. In fact, due to the process being used in the data sampling procedure, e.g. the analytical equipment, only the measurements which exceed some thresholds, for example the method detection limits or the instrumental detection limits, can be included in the data analysis. Many other examples can also be found in other fields like econometrics and financial statistics. Observations on duration of unemployment e.g., may be right censored and are typically correlated. When the data are not independent and are subject to censoring, estimation and inference become more challenging mathematical problems with a wide area of applications. In this context, we propose here some new and flexible tools based on a nonparametric approach. More precisely, allowing dependence between individuals, our main contribution to this domain concerns the following aspects. First, we are interested in developing more suitable confidence intervals for a general class of functionals of a survival distribution via the empirical likelihood method. Secondly, we study the problem of conditional mean estimation using the local linear technique. Thirdly, we develop and study a new estimator of the conditional quantile function also based on the local linear method. In this dissertation, for each proposed method, asymptotic results like consistency and asymptotic normality are derived and the finite sample performance is evaluated in a simulation study.

A frequent problem that appears in practical survival data analysis is censoring. A censored observation occurs when the observation of the event time (duration or survival time) may be prevented by the occurrence of an earlier competing event (censoring time). Censoring may be due to different causes. For example, the loss of some subjects under study, the end of the follow-up period, drop out or the termination of the study and the limitation in the sensitivity of a measurement instrument. The literature about censored data focuses on the i.i.d. case. However in many real applications the data are collected sequentially in time or space and so the assumption of independence in such case does not hold. Here we only give some typical examples from the literature involving correlated data which are subject to censoring. In the clinical trials domain it frequently happens that the patients from the same hospital have correlated survival times due to unmeasured variables like the quality of the hospital equipment. Censored correlated data are also a common problem in the domain of environmental and spatial (geographical or ecological) statistics. In fact, due to the process being used in the data sampling procedure, e.g. the analytical equipment, only the measurements which exceed some thresholds, for example the method detection limits or the instrumental detection limits, can be included in the data analysis. Many other examples can also be found in other fields like econometrics and financial statistics. Observations on duration of unemployment e.g., may be right censored and are typically correlated. When the data are not independent and are subject to censoring, estimation and inference become more challenging mathematical problems with a wide area of applications. In this context, we propose here some new and flexible tools based on a nonparametric approach. More precisely, allowing dependence between individuals, our main contribution to this domain concerns the following aspects. First, we are interested in developing more suitable confidence intervals for a general class of functionals of a survival distribution via the empirical likelihood method. Secondly, we study the problem of conditional mean estimation using the local linear technique. Thirdly, we develop and study a new estimator of the conditional quantile function also based on the local linear method. In this dissertation, for each proposed method, asymptotic results like consistency and asymptotic normality are derived and the finite sample performance is evaluated in a simulation study.

The objective of the present study is to investigate various problems associate with breast cancer and lung cancer patients. In this study, we compare the effectiveness of breast cancer treatments using decision tree analysis and come to the conclusion that although certain treatment shows overall effectiveness over the others, physicians or doctors should discretionally give different treatment to breast cancer patients based on their characteristics. Reoccurrence time of breast caner patients who receive different treatments are compared in an overall sense, histology type is also taken into consideration. To further understand the relation between relapse time and other variables, statistical models are applied to identify the attribute variables and predict the relapse time. Of equal importance, the transition between different breast cancer stages are analyzed through Markov Chain which not only gives the transition probability between stages for specific treatment but also provide guidance on breast cancer treatment based on stating information. Sensitivity analysis is conducted on breast cancer doubling time which involves two commonly used assumptions: spherical tumor and exponential growth of tumor and the analysis reveals that variation from those assumptions could cause very different statistical behavior of breast cancer doubling time. In lung cancer study, we investigate the mortality time of lung cancer patients from several different perspectives: gender, cigarettes per day and duration of smoking. Statistical model is also used to predict the mortality time of lung cancer patients.