Submitted by Claudinei Pracidelli (cpracide@ipen.br) on 2015-05-20T17:36:43Z No. of bitstreams: 0 / Made available in DSpace on 2015-05-20T17:36:43Z (GMT). No. of bitstreams: 0 / A radioterapia é uma das três principais modalidades utilizadas no tratamento de doenças malignas como o câncer; as outras duas são a quimioterapia e a radiocirurgia. Em contraste com outras especialidades médicas que necessitam principalmente do conhecimento clínico e da experiência de especialistas, a radioterapia, com a utilização da radiação ionizante no tratamento do câncer, depende do investimento pesado em tecnologias modernas e dos esforços colaborativos de diversos profissionais, cuja equipe coordenada influencia, sobremaneira, o resultado do tratamento. A Radioterapia de intensidade modulada (IMRT) com o uso de colimadores multilâminas (multileaf collimators MLCs) tem o potencial para alcançar um alto grau de conformidade da dose no alvo (tumor a ser tratado) e ainda promover a proteção de tecidos normais do que a maioria de outras técnicas de tratamento, especialmente para volumes-alvo ou órgãos de risco com formatos complexos. Entretanto, estudos recentes mostraram que baixas doses de radiação podem causar tumores secundários. Esse trabalho tem como objetivo a determinação da distribuição de dose de radiação absorvida em diversas simulações de tratamentos radioterápicos com o uso de dosímetros compostos de LiF:Mg,Ti; CaSO4:Dy e Al2O3:C, utilizando um objeto simulador de polimetilmetacrilato (PMMA) empregando as seguintes técnicas dosimétricas: termoluminescência (TL), luminescência opticamente estimulada (OSL) e detectores semicondutores (diodos). / Tese (Doutorado em Tecnologia Nuclear) / IPEN/T / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

Orientadores: Michel Daoud Yacoub, Jose Candido Silveira Santos Filho / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-12T20:13:16Z (GMT). No. of bitstreams: 1 BarrosTercius_Hermano_M.pdf: 3346341 bytes, checksum: 1398a699b8f875964e6bee207c3bf263 (MD5) Previous issue date: 2008 / Resumo: Esta dissertação provê uma comparação detalhada entre diversas distribuições de desvanecimento de curto prazo, que são trabalhadas de forma a propiciar seus melhores ajustes para dados obtidos a partir de medições de campo. Tais distribuições incluem aquelas já bem conhecidas na literatura - como Rayleigh, Rice, Nakagami, Hoyt e Weibull - bem como dois modelos propostos recentemente, especificamente k-µ e h-µ. Primeiramente, é apresentado um embasamento teórico sobre estas distribuições. Em seguida, são fornecidos todos os detalhes da construção e dos ajustes dos parâmetros do aparato de medições, bem como os procedimentos de coleta dos dados. Então, os diversos resultados das medições são analisados. As análises são baseadas em um vasto espaço amostral, englobando 300 ambientes investigados. Sob a óptica de uma de jornada de medição tão ampla, é possível recomendar o uso adequado destas distribuições recém-propostas, nomeadamente k-µ e h-µ. Pelos resultados obtidos, pode-se constatar que uma melhora significativa no processo de ajuste de curva é obtida com a utilização destas distribuições, as quais podem ser úteis para uma modelagem adequada do ambiente de propagação das comunicações sem fio / Abstract: This thesis provides a thorough comparison among the various short term fading distributions, which are handled so as to give their best fit over data obtained from field measurements. Such distributions include those already well known in the literature - namely Rayleigh, Rice, Nakagami, Hoyt e Weibull - as well as two recently proposed models, namely k-µ and h-µ. Firstly, some theoretical background concerning these distributions are given. Then, the details of the construction and required parameters adjustments of the measurement apparatus as well as the data collection procedures are shown. Then, the various results of the measurements are analysed. The analyses are based on an ample sample space, encompassing 300 surveying sites. In the view of such a vast measurement campaign, it may be possible to recommend the adequate use of those newly proposed distributions, namely k-µ e h-µ. From our results, it is possible to see that a significant improvement in the curve fitting process can be seen with the use of these distributions, which may be useful for the adequate modelling of the wireless communications propagation environement / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica

Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2017-09-22T19:42:42Z No. of bitstreams: 2 Dissertação - Leandro Valerio Silva - 2017.pdf: 5149451 bytes, checksum: 3c193b0556f60d79c470ef940136aaf7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-09-25T11:46:04Z (GMT) No. of bitstreams: 2 Dissertação - Leandro Valerio Silva - 2017.pdf: 5149451 bytes, checksum: 3c193b0556f60d79c470ef940136aaf7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-09-25T11:46:04Z (GMT). No. of bitstreams: 2 Dissertação - Leandro Valerio Silva - 2017.pdf: 5149451 bytes, checksum: 3c193b0556f60d79c470ef940136aaf7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-03-25 / The aim of this study is to use control charts to monitor the ICMS revenue in Goias, Brazil. The autocorrelation of the data was removed by using symmetric autoregressive moving average (SYMARMA) models. The results showed that the Shewhart control chart obtained from the SYMARMA model, based on a conditional normal distribuition, presented the best result. / O objetivo principal deste estudo é construir gráficos de controle para o monitoramento da arrecadação de ICMS em Goiás. Para tal, foram utilizados modelos autoregressivos de médias móveis simétricos (SYMARMA) com a finalidade de eliminar a autocorrelação presente na série temporal dos recolhimentos de ICMS. Construíram-se gráficos de controle do tipo Shewhart, CUSUM e EWMA, a partir dos resíduos gerados pelo modelo SYMARMA. Os resultados demonstraram que o gráfico de controle de Shewhart construído a partir dos resíduos do modelo SYMARMA com distribuição normal condicional apresentou-se o mais de acordo com a realidade da arrecadação.

As relações de dependência entre variáveis aleatórias é um dos assuntos mais discutidos em probabilidade e estatística e a forma mais abrangente de estudar essas relações é por meio da distribuição conjunta. Nos últimos anos vem crescendo a utilização de cópulas para representar a estrutura de dependência entre variáveis aleatórias em uma distribuição multivariada. Contudo, ainda existe pouca literatura sobre cópulas quando as distribuições marginais são discretas. No presente trabalho será apresentada uma proposta não-paramétrica de estimação da distribuição conjunta bivariada de variáveis aleatórias discretas utilizando cópulas e polinômios de Bernstein. / The relations of dependence between random variables is one of the most discussed topics in probability and statistics and the best way to study these relationships is through the joint distribution. In the last years has increased the use of copulas to represent the dependence structure among random variables in a multivariate distribution. However, there is still little literature on copulas when the marginal distributions are discrete. In this work we present a non-parametric approach for the estimation of the bivariate joint distribution of discrete random variables using copulas and Bernstein polynomials.

Este trabalho tem como objetivo calcular intervalos de confiança para altos quantis oriundos de distribuições de caudas pesadas. Para isso, utilizamos os métodos da aproximação pela distribuição normal, razão de verossimilhanças, {\\it data tilting} e gama generalizada. Obtivemos, através de simulações, que os intervalos calculados a partir do método da gama generalizada apresentam probabilidades de cobertura bem próximas do nível de confiança, com amplitudes médias menores do que os outros três métodos, para dados gerados da distribuição Weibull. Todavia, para dados gerados da distribuição Fréchet, o método da razão de verossimilhanças fornece os melhores intervalos. Aplicamos os métodos utilizados neste trabalho a um conjunto de dados reais, referentes aos pagamentos de indenizações, em reais, de seguros de incêndio, de um determinado grupo de seguradoras no Brasil, no ano de 2003 / In this work, confidence intervals for high quantiles from heavy-tailed distributions were computed. More specifically, four methods, namely, normal approximation method, likelihood ratio method, data tilting method and generalised gamma method are used. A simulation study with data generated from Weibull distribution has shown that the generalised gamma method has better coverage probabilities with the smallest average length intervals. However, from data generated from Fréchet distribution, the likelihood ratio method gives the better intervals. Moreover, the methods used in this work are applied on a real data set from 1758 Brazilian fire claims

With growing markets and customers being geographically spread out, more pressure is put on a company’s logistics processes and their inventory structures are becoming more complex. This puts more pressure on the inventory control solution provided by a company like IFS, that supports their customers with inventory control through the Inventory Planning and Replenishment module in IFS Applications. As their customers’ supply chains grow larger, their inventory structures become more complex the next step is to find a solution for the IPR module more suitable in a called multi-echelon structure, i.e. several tiers of stock locations, such as local, regional and central warehouses.   The purpose of this study is to compare a reorder point model with a solution suitable in a multi-echelon setting and investigate how they are able to manage uncertainties with service level targets.   A literature study was performed, to find previous research on inventory control in multi-echelon inventory systems. In the literature study, the importance of coordination and information sharing between the echelons was emphasized and used as a focus when finding a suitable multi-echelon model. To answer the purpose a theoretical model was formulated from the findings in previous research, with a replenishment method suitable in a multi-echelon environment. The inventory control models also included lot sizing method and a safety mechanism, where the difference between the models were their respective replenishment policy. The theoretical model was based on the replenishment method Distribution Requirements Planning (DRP), as it enables information sharing, coordination and synchronization of the supply chain, while the other inventory control model uses the Reorder Point method (ROP).   As information sharing was emphasized in previous research on multi-echelon systems, and the main difference between the two inventory control models is the information sharing in the DRP model, the important question to be answered with the comparison is; what effects and benefits can be achieved through information sharing in a multi-echelon inventory system? The two inventory control models were then simulated in Excel and exposed to even demand and seasonal variations in an inventory structure with three echelons and four sites, see figure below. When analyzing the results three evaluation criteria were used; difference in service levels, average inventory levels and if there were signs of overstocking in the regional and central warehouse, i.e. if the system was exposed to the bullwhip effect.   The analysis was carried out based on the criteria above and divided into three sections. First, differences between the models for even demand were investigated. The same procedure followed for seasonal demand, identifying differences and what caused them. Findings were then summed up at the end of the chapter. For even demand, differences were small and sharing information does not give large benefits. Under seasonal demand though, sharing information proved to be very beneficial, reducing average inventory held in the system by 60%, compared to not sharing information. This because sharing information together with synchronizing eliminates the bullwhip effect.   By testing different standard deviations, changing lead times and order quantities, using forecast or being blind to forecast, the robustness of the conclusions drawn from the analysis was put to the test. Carrying out a sensitivity analysis on the models served two purposes. First, finding more evidence promoting the benefits of synchronizing the supply chain and how important it is that the shared information is correct, otherwise the benefits are reduced. The second purpose was to validate that the models performed as expected when changing input data.   The conclusions were the following:   Information sharing enables synchronization of the supply chain Synchronization allows for reaching higher service levels with lower inventory levels   Findings suggest that by sharing information, which must be the first step, synchronizing the inventory system is possible. It is the synchronization that creates the real benefits, such as higher service levels and lower inventory levels. However, the quality and accuracy of the shared information was found to play an important role. Sharing inaccurate or wrong information increase the risk of the system starting to suffer from the bullwhip effect, resulting in higher inventory levels and lower service levels.

No description available.

Substance use data such as alcohol drinking often contain a high proportion of zeros. In studies examining the alcohol consumption in college students, for instance, many students may not drink in the studied period, resulting in a number of zeros. Zero-inflated continuous data, also called semi continuous data, typically consist of a mixture of a degenerate distribution at the origin (zero) and a right-skewed, continuous distribution for the positive values. Ignoring the extreme non-normality in semi-continuous data may lead to substantially biased estimates and inference. Longitudinal or repeated measures of semi-continuous data present special challenges in statistical inference because of the correlation tangled in the repeated measures on the same subject. Linear mixed-eects models (LMM) with normality assumption that is routinely used to analyze correlated continuous outcomes are inapplicable for analyzing semi-continuous outcome. Data transformation such as log transformation is typically used to correct the non-normality in data. However, log-transformed data, after the addition of a small constant to handle zeros, may not successfully approximate the normal distribution due to the spike caused by the zeros in the original observations. In addition, the reasons that data transformation should be avoided include: (i) transforming usually provides reduced information on an underlying data generation mechanism; (ii) data transformation causes diculty in regard to interpretation of the transformed scale; and (iii) it may cause re-transformation bias. Two-part mixed-eects models with one component modeling the probability of being zero and one modeling the intensity of nonzero values have been developed over the last ten years to analyze the longitudinal semi-continuous data. However, log transformation is still needed for the right-skewed nonzero continuous values in the two-part modeling. In this research, we developed Bayesian hierarchical models in which the extreme non-normality in the longitudinal semi-continuous data caused by the spike at zero and right skewness was accommodated using skew-elliptical (SE) distribution and all of the inferences were carried out through Bayesian approach via Markov chain Monte Carlo (MCMC). The substance abuse/dependence data, including alcohol abuse/dependence symptoms (AADS) data and marijuana abuse/dependence symptoms (MADS) data from a longitudinal observational study, were used to illustrate the proposed models and methods. This dissertation explored three topics: First, we presented one-part LMM with skew-normal (SN) distribution under Bayesian framework and applied it to AADS data. The association between AADS and gene serotonin transporter polymorphism (5-HTTLPR) and baseline covariates was analyzed. The results from the proposed model were compared with those from LMMs with normal, Gamma and LN distributional assumptions. Simulation studies were conducted to evaluate the performance of the proposed models. We concluded that the LMM with SN distribution not only provides the best model t based on Deviance Information Criterion (DIC), but also offers more intuitive and convenient interpretation of results, because it models the original scale of response variable. Second, we proposed a flexible two-part mixed-effects model with skew distributions including skew-t (ST) and SN distributions for the right-skewed nonzero values in Part II of model under a Bayesian framework. The proposed model is illustrated with the longitudinal AADS data and the results from models with ST, SN and normal distributions were compared under different random-effects structures. Simulation studies are conducted to evaluate the performance of the proposed models. Third, multivariate (bivariate) correlated semi-continuous data are also commonly encountered in clinical research. For instance, the alcohol use and marijuana use may be observed in the same subject and there might be underlying common factors to cause the dependence of alcohol and marijuana uses. There is very limited literature on multivariate analysis of semi-continuous data. We proposed a Bayesian approach to analyze bivariate semi-continuous outcomes by jointly modeling a logistic mixed-effects model on zero-inflation in either response and a bivariate linear mixed-effects model (BLMM) on the positive values through a correlated random-effects structure. Multivariate skew distributions including ST and SN distributions were used to relax the normality assumption in BLMM. The proposed models were illustrated with an application to the longitudinal AADS and MADS data. A simulation study was conducted to evaluate the performance of the proposed models.

Please read the abstract in the section 00front of this document / Dissertation (MSc (Industrial Systems))--University of Pretoria, 2006. / Industrial and Systems Engineering / unrestricted

The multinomial logit model (MNL) possesses a latent variable representation in terms of random variables following a multivariate logistic distribution. Based on multivariate finite mixture approximations of the multivariate logistic distribution, various data-augmented Metropolis-Hastings algorithms are developed for a Bayesian inference of the MNL model.