O cenário agrícola nacional tem se tornado cada vez mais competitivo ao longo dos anos, manter o crescimento da produtividade a um baixo custo operacional e com baixo impacto ambiental tem sido os três ingredientes de maior relevância na área. A produtividade por sua vez, é função de várias variáveis, sendo o controle de plantas daninhas uma dessas variáveis a ser considerada. Nesse trabalho é analisado um conjunto de dados de um experimento realizado no departamento de Produção Vegetal da ESALQ-USP, Piracicaba - SP. Foram avaliadas 4 biótipos de capim-pé-de-galinha provenientes de três estados brasileiros e em três estágios morfológicos com 4 repetições para cada biótipo, a variável resposta utilizada foi massa seca (g) e como variável regressora foi utilizada a dose de glyphosate nas concentrações variando de 1/16 D a 16 D mais a testemunha, sem aplicação de herbicida, em que D varia de 480 gramas de equivalente ácido de glyphosate por hectare (g .e a. ha-1) para o estágio de 2 a 3 perfilhos, 720 (g .e a. ha-1) para o estágio de 6 a 8 perfilhos e de 960 para o estágio de 10-12 perfilhos. O trabalho teve como objetivo primário avaliar se, ao longo dos anos, as populações de capim-pé-de-galinha tem se tornado resistentes ao herbicida glyphosate, visando detecção de biótipos resistentes. O experimento foi instalado segundo o delineamento inteiramente aleatorizado, sendo feito em três estágios diferentes. Para a análise dos dados foi utilizado o modelo não-linear log-logístico proposto em Knezevic, S. e Ritz (2007) como método univariado, foi utilizado ainda o método da máxima verossimilhança para verificar a igualdade do parâmetro e. O modelo utilizado convergiu para quase todas as repetições, mas não houve um comportamento sistemático observado que explicasse a não convergência de uma repetição em particular. Num segundo momento, as estimativas dos três parâmetros do modelo foram tomadas como variáveis dependentes em uma análise de variância multivariada. Observando que as três, conjuntamente, foram significativas pelos testes de Pillai, Wilks, Roy e Hotelling-Lawley, foi realizado o teste de Tukey para o mesmo parâmetro e comparado com o primeiro método utilizado. Esse procedimento apresentou, com o mesmo coeficiente de significância, menor capacidade de identificar diferença entre as médias dos parâmetros das variedades de capim do que o método proposto por Regazzi (2015). / The national agricultural scenery has become increasingly competitive over the years, maintaining productivity growth at a low operating cost and low environmental impact has been the three most important ingredients in the area. Productivity in turn is a function of several variables, and the weed control is one of these variables to be considered. In this work it is analyzed a dataset of an experiment conducted in the Plant Production Department of ESALQ-USP, Piracicaba - SP. Were evaluated 4 grass chicken\'s feet biotypes from three Brazilian states in three morphological stages with 4 repetitions for each biotype, the response variable used was dry mass (g) and as regressor variable were used the dose of glyphosate in concentrations ranging from 1/16 D to 16 D plus the control without herbicide, wherein D ranges from 480 grams of glyphosate acid equivalent per hectare (g .e a. ha-1) for 2 to 3 stage tillers, 720 grams of glyphosate acid equivalent per hectare (g .e a. ha-1) for 6 to 8 tillers and 960 for stage 10-12 tillers. The work had as main objective to evaluate , if over the years, populations of grass chicken\'s feet has become resistant to glyphosate, aiming detection of resistant biotypes. The experiment was conducted under completely randomized design being done in three stages. For data analysis was used the non-linear log-logistic proposed in Knezevic, S. e Ritz (2007) as univariate method, it was still used the maximum likelihood method to verify the equality of the parameter e. The model converged to almost all repetitions, but there was an observed systematic behavior to explain the non-convergence of a particular repetition. Secondly, estimates of the three model parameters were taken as dependent variables in a multivariate analysis of variance. Noting that all three together, were significant by Pillai, Wilks, Roy and Hotelling-Lawley tests, was performed Tukey test for the same parameter e and compared with the first method. This procedure presented, with the same coefficient of significance, less able to identify differences between the means of the parameters of grass varieties than the method proposed by Regazzi (2015).

Improvements in printing technology have exacerbated the problem of document counterfeiting, prompting the need for analytical techniques that better characterize inks for forensic analysis. In this study, 319 printing inks (toner, inkjet, offset, and intaglio) were analyzed directly on the paper substrate using Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS) and Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS). As anticipated, the high sensitivity of LA-ICP-MS resulted in excellent discrimination (> 99%) between ink samples originating from different sources. Moreover, LA-ICP-MS provided ≥ 90% correct association for ink samples originating from the same source. SEM-EDS resulted in good discrimination for toner and intaglio inks (> 97%) and excellent correct association (100%) for all four ink types. However, the technique showed limited utility for the discrimination of inkjet and offset inks. A searchable ink database, the Forensic Ink Analysis and Comparison System (FIACS), was developed in order to provide a tool that allows the analyst to compare a questioned ink sample to a reference population. The FIACS database provided a correct classification rate of 94-100% for LA-ICP-MS and 67-100% for SEM-EDS. An important consideration in forensic chemistry is the interpretation of the evidence. Typically, a match criterion is used to compare the known and questioned sample. However, this approach suffers from several disadvantages, which can be overcome with an alternative approach: the likelihood ratio (LR). Two LA-ICP-MS glass databases were used to evaluate the performance of the LR: a vehicle windshield database (420 samples) and a casework database (385 samples). Compared to the match criterion, the likelihood ratio led to improved false exclusion rates (< 1.5%) and similar false inclusion rates (< 1.0%). In addition, the LR limited the magnitude of the misleading evidence, providing only weak support for the incorrect proposition. The likelihood ratio was also tested through an inter-laboratory study including 10 LA-ICP-MS participants. Good correct association rates (94-100%) were obtained for same-source samples for all three inter-laboratory exercises. Moreover, the LR showed a strong support for an association. Finally, all different-source samples were correctly excluded with the LR, resulting in no false inclusions.

A semiparametric proportional likelihood ratio model was proposed by Luo and Tsai (2012) which is suitable for modeling a nonlinear monotonic relationship between the response variable and a covariate. Extending the generalized linear model, this model leaves the probability distribution unspecified but estimates it from the data. In this thesis, we propose to extend this model into analyzing the longitudinal data by incorporating random effects into the linear predictor. By using this model as the conditional density of the response variable given the random effects, we present a maximum likelihood approach for model estimation and inference. Two numerical estimation procedures were developed for response variables with finite support, one based on the Newton-Raphson algorithm and the other one based on generalized expectation maximization (GEM) algorithm. In both estimation procedures, Gauss-Hermite quadrature is employed to approximate the integrals. Upon convergence, the observed information matrix is estimated through the second-order numerical differentiation of the log likelihood function. Asymptotic properties of the maximum likelihood estimator are established under certain regularity conditions and simulation studies are conducted to assess its finite sample properties and compare the proposed model to the generalized linear mixed model. The proposed method is illustrated in an analysis of data from a multi-site observational study of prodromal Huntington's disease.

This research study is the result of a 3 years CIFRE PhD thesis between the Airbus design office(Aircraft Control domain) and TéSA laboratory in Toulouse. The main goal is to propose, developand validate a software solution for the detection and diagnosis of a specific type of elevator andrudder vibration, called limit cycle oscillation (LCO), based on existing signals available in flightcontrol computers on board in-series aircraft. LCO is a generic mathematical term defining aninitial condition-independent periodic mode occurring in nonconservative nonlinear systems. Thisstudy focuses on the LCO phenomenon induced by mechanical freeplays in the control surface ofa civil aircraft. The LCO consequences are local structural load augmentation, flight handlingqualities deterioration, actuator operational life reduction, cockpit and cabin comfort deteriorationand maintenance cost augmentation. The state-of-the-art for freeplay induced LCO detection anddiagnosis is based on the pilot sensitivity to vibration and to periodic freeplay check on the controlsurfaces. This study is thought to propose a data-driven solution to help LCO and freeplaydiagnosis. The goal is to improve even more aircraft availability and reduce the maintenance costsby providing to the airlines a condition monitoring signal for LCO and freeplays. For this reason,two algorithmic solutions for vibration and freeplay diagnosis are investigated in this PhD thesis. Areal time detector for LCO diagnosis is first proposed based on the theory of the generalized likeli hood ratio test (GLRT). Some variants and simplifications are also proposed to be compliantwith the industrial constraints. In a second part of this work, a mechanical freeplay detector isintroduced based on the theory of Wiener model identification. Parametric (maximum likelihoodestimator) and non parametric (kernel regression) approaches are investigated, as well as somevariants to well-known nonparametric methods. In particular, the problem of hysteresis cycleestimation (as the output nonlinearity of a Wiener model) is tackled. Moreover, the constrainedand unconstrained problems are studied. A theoretical, numerical (simulator) and experimental(flight data and laboratory) analysis is carried out to investigate the performance of the proposeddetectors and to identify limitations and industrial feasibility. The obtained numerical andexperimental results confirm that the proposed GLR test (and its variants/simplifications) is a very appealing method for LCO diagnostic in terms of performance, robustness and computationalcost. On the other hand, the proposed freeplay diagnostic algorithm is able to detect relativelylarge freeplay levels, but it does not provide consistent results for relatively small freeplay levels. Moreover, specific input types are needed to guarantee repetitive and consistent results. Further studies should be carried out in order to compare the GLRT results with a Bayesian approach and to investigate more deeply the possibilities and limitations of the proposed parametric method for Wiener model identification.

In instances of mass fatality, such as plane crashes, natural disasters, or terrorist attacks, investigators may encounter hundreds or thousands of DNA specimens representing victims. For example, during the January 2010 Haiti earthquake, entire communities were destroyed, resulting in the loss of thousands of lives. With such a large number of victims the discovery of family pedigrees is possible, but often requires the manual application of analytical methods, which are tedious, time-consuming, and expensive. The method presented in this thesis allows for automated pedigree discovery by extending Link Discovery Tool (LDT), a graph visualization tool designed for discovering linkages in large criminal networks. The proposed algorithm takes advantage of spatial clustering of graphs of DNA specimens to discover pedigree structures in large collections of specimens, saving both time and money in the identification process.

We provide ways to test the fit of a parametric copula family for bivariate censored data with or without covariates. The proposed copula family is tested by embedding it in an expanded parametric family of copulas. When parameters in the proposed and the expanded copula models are estimated by maximum likelihood, a likelihood ratio test can be used. However, when they are estimated by two-stage pseudolikelihood estimation, the corresponding test is a pseudolikelihood ratio test. The two-stage procedures offer less computation, which is especially attractive when the marginal lifetime distributions are specified nonparametrically or semiparametrically. It is shown that the likelihood ratio test is consistent even when the expanded model is misspecified. Power comparisons of the likelihood ratio and the pseudolikelihood ratio tests with some other goodness-of-fit tests are performed both when the expanded family is correct and when it is misspecified. They indicate that model expansion provides a convenient, powerful and robust approach. We introduce a semiparametric maximum likelihood estimation method in which the copula parameter is estimated without assumptions on the marginal distributions. This method and the two-stage semiparametric estimation method suggested by Shih and Louis (1995) are generalized to regression models with Cox proportional hazards margins. The two-stage semiparametric estimator of the copula parameter is found to be about as good as the semiparametric maximum likelihood estimator. Semiparametric likelihood ratio and pseudolikelihood ratio tests are considered to provide goodness of fit tests for a copula model without making parametric assumptions for the marginal distributions. Both when the expanded family is correct and when it is misspecified, the semiparametric pseudolikelihood ratio test is almost as powerful as the parametric likelihood ratio and pseudolikelihood ratio tests while achieving robustness to the form of the marginal distributions. The methods are illustrated on applications in medicine and insurance. Sequentially observed survival times are of interest in many studies but there are difficulties in modeling and analyzing such data. First, when the duration of followup is limited and the times for a given individual are not independent, the problem of induced dependent censoring arises for the second and subsequent survival times. Non-identifiability of the marginal survival distributions for second and later times is another issue, since they are observable only if preceding survival times for an individual are uncensored. In addition, in some studies, a significant proportion of individuals may never have the first event. Fully parametric models can deal with these features, but lack of robustness is a concern, and methods of assessing fit are lacking. We introduce an approach to address these issues. We model the joint distribution of the successive survival times by using copula functions, and provide semiparametric estimation procedures in which copula parameters are estimated without parametric assumptions on the marginal distributions. The performance of semiparametric estimation methods is compared with some other estimation methods in simulation studies and shown to be good. The methodology is applied to a motivating example involving relapse and survival following colon cancer treatment.

We provide ways to test the fit of a parametric copula family for bivariate censored data with or without covariates. The proposed copula family is tested by embedding it in an expanded parametric family of copulas. When parameters in the proposed and the expanded copula models are estimated by maximum likelihood, a likelihood ratio test can be used. However, when they are estimated by two-stage pseudolikelihood estimation, the corresponding test is a pseudolikelihood ratio test. The two-stage procedures offer less computation, which is especially attractive when the marginal lifetime distributions are specified nonparametrically or semiparametrically. It is shown that the likelihood ratio test is consistent even when the expanded model is misspecified. Power comparisons of the likelihood ratio and the pseudolikelihood ratio tests with some other goodness-of-fit tests are performed both when the expanded family is correct and when it is misspecified. They indicate that model expansion provides a convenient, powerful and robust approach. We introduce a semiparametric maximum likelihood estimation method in which the copula parameter is estimated without assumptions on the marginal distributions. This method and the two-stage semiparametric estimation method suggested by Shih and Louis (1995) are generalized to regression models with Cox proportional hazards margins. The two-stage semiparametric estimator of the copula parameter is found to be about as good as the semiparametric maximum likelihood estimator. Semiparametric likelihood ratio and pseudolikelihood ratio tests are considered to provide goodness of fit tests for a copula model without making parametric assumptions for the marginal distributions. Both when the expanded family is correct and when it is misspecified, the semiparametric pseudolikelihood ratio test is almost as powerful as the parametric likelihood ratio and pseudolikelihood ratio tests while achieving robustness to the form of the marginal distributions. The methods are illustrated on applications in medicine and insurance. Sequentially observed survival times are of interest in many studies but there are difficulties in modeling and analyzing such data. First, when the duration of followup is limited and the times for a given individual are not independent, the problem of induced dependent censoring arises for the second and subsequent survival times. Non-identifiability of the marginal survival distributions for second and later times is another issue, since they are observable only if preceding survival times for an individual are uncensored. In addition, in some studies, a significant proportion of individuals may never have the first event. Fully parametric models can deal with these features, but lack of robustness is a concern, and methods of assessing fit are lacking. We introduce an approach to address these issues. We model the joint distribution of the successive survival times by using copula functions, and provide semiparametric estimation procedures in which copula parameters are estimated without parametric assumptions on the marginal distributions. The performance of semiparametric estimation methods is compared with some other estimation methods in simulation studies and shown to be good. The methodology is applied to a motivating example involving relapse and survival following colon cancer treatment.

To combat channel fading, cooperative communication achieves spatial diversity for the transmission between source and destination through the help of relay. However, if the relay behaves abnormally or maliciously and the destination is not aware, the diversity gain of the cooperative system will be significantly reduced, which degrades system performance. In our thesis, we consider an one-relay decode and forward cooperative network, and we assume that the relay may misbehave with a certain probability. If the relay is malicious, it will garble transmission signal, resulting in severe damage to cooperative system. In this work, we discuss three kinds of malicious behavior detection. More specifically, we adopt sequential detection to detect the behavior of relay. If tracing symbols are inserted among the source message, the destination detects malicious after extracting the received tracing symbols. We adopt log-likelihood ratio test to examine these tracing symbols, and then determine the behavior of relay. If the source does not transmit tracing symbols, the destination detects misbehavior according to the received data signal. Furthermore, we employ sequential detection to reduce detection time for a given probabilities of false alarm and miss detection. Through simulation results, for a certain target on probability of errors, our proposed methods can effectively reduce numbers of observations. On the other works, the destination can effectively detect misbehavior of relay, and eliminating the damage causes by malicious relay without requiring large numbers of observations.

Surgeon operation time is a useful and important information for hospital management, which involves operation time estimation for patients under different diagnoses, operation room scheduling, operating room utilization improvements and so on. In this work, we will focus on studying the operation time distributions of thirteen operations performed in the gynecology (GYN) department of one major teaching hospital in southern Taiwan. We firstly investigate what types of distributions are suitable in describing these operation times empirically, where log-normal and mixture log-normal distribution are identified to be acceptable statistically in describing these operation times. Then we compare and characterize the operations into different categories based on the operation time distribution estimates. Later we try to illustrate the possible reason why distributions for some operations with large data set turn out to be mixture of certain log-normal distributions. Finally we end with discussions on possible future work.

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