Modelos para dados binários são bastante utilizados em várias situações práticas. Transformações em Análise de Regressão podem ser aplicadas para linearizar ou simplificar o modelo e também para corrigir desvios de suposições. Neste trabalho, descrevemos o uso de transformações nos modelos de regressão logística para dados binários e apresentamos modelos envolvendo parâmetros adicionais de modo a obter um ajuste mais adequado. Posteriormente, analisamos o custo da estimação quando são adicionados parâmetros aos modelos e apresentamos os testes de hipóteses relativos aos parâmetros do modelo de regressão logística de Box-Cox. Finalizando, apresentamos alguns métodos de diagnóstico para avaliar a influência das observações nas estimativas dos parâmetros de transformação da covariável, com aplicação a um conjunto de dados reais. / Binary data models have a lot of utilities in many practical situations. In Regrssion Analisys, transformations can be applied to linearize or simplify the model and correct deviations of the suppositions. In this dissertation, we show the use of the transformations in logistic models to binary data models and models involving additional parameters to obtain more appropriate fits. We also present the cost of the estimation when parameters are added to models, hypothesis tests of the parameters in the Box-Cox logistic regression model and finally, diagnostics methods to evaluate the influence of the observations in the estimation of the transformation covariate parameters with their applications to a real data set.

Modelos para dados binários são bastante utilizados em várias situações práticas. Transformações em Análise de Regressão podem ser aplicadas para linearizar ou simplificar o modelo e também para corrigir desvios de suposições. Neste trabalho, descrevemos o uso de transformações nos modelos de regressão logística para dados binários e apresentamos modelos envolvendo parâmetros adicionais de modo a obter um ajuste mais adequado. Posteriormente, analisamos o custo da estimação quando são adicionados parâmetros aos modelos e apresentamos os testes de hipóteses relativos aos parâmetros do modelo de regressão logística de Box-Cox. Finalizando, apresentamos alguns métodos de diagnóstico para avaliar a influência das observações nas estimativas dos parâmetros de transformação da covariável, com aplicação a um conjunto de dados reais. / Binary data models have a lot of utilities in many practical situations. In Regrssion Analisys, transformations can be applied to linearize or simplify the model and correct deviations of the suppositions. In this dissertation, we show the use of the transformations in logistic models to binary data models and models involving additional parameters to obtain more appropriate fits. We also present the cost of the estimation when parameters are added to models, hypothesis tests of the parameters in the Box-Cox logistic regression model and finally, diagnostics methods to evaluate the influence of the observations in the estimation of the transformation covariate parameters with their applications to a real data set.

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This project implemented novel approaches to assess the source, age, concentration and mobility of fecal indicator microorganisms within a karst groundwater system. Research was conducted in the well-characterized Blue Hole Spring karst groundwater basin in Versailles, Woodford County, Kentucky. At this site the AC/TC ratio and fecal coliform (FC) bacteria counts were used to delineate sources of fecal inputs and determine relative age of the fecal matter. An aging experiment using indicator bacteria (total coliform (TC) and atypical colonies (AC)), which approximated subsurface conditions, indicated that changes in the AC/TC ratio are likely to be retarded during bacterial transport through karst conduits. Decreases in the AC/TC ratio during the monitoring period appear to be the result of sewage releases. Multiple logistic regression (MLR) modeling was performed to examine correlations between physiochemical parameters and FC concentrations. MLR models using physiochemical parameters correctly predicted “safe for contact” (< 200 cfu/100 mL FC) conditions 65.6% of the time and “unsafe for contact” (> 200 cfu/100 mL FC) conditions 69.2% of the time at Blue Hole Spring. Modeling using other indicators (TC and AC) predicted “safe for contact” conditions 87.5% of the time and “unsafe for contact” conditions 61.5% of the time. A series of tracer tests were performed to compare transport of solute and abiotic particle tracers (rhodamine WT fluorescent dye, bromide and fluorescent bacteria-sized microspheres) and bacteria (15N-enriched wild-type E. coli) within the karst system. The surrogate tracers did not suitably mimic microbial mobility within the basin. Solutes and 15N-enriched E. coli arrived concurrently during storm flow to Blue Hole Spring, whereas microsphere breakthrough corresponded with maximum solute concentrations. The 15Nenriched E. coli exhibited slightly more tailing during storm-flow recession than solute tracers, none of which exhibited remobilization. Microspheres demonstrated remobilization within the conduits that correlated with later increases in discharge related to secondary storm events.

Panel data, also known as longitudinal data, are composed of repeated measurements taken from the same subject over different time points. Although it is generally used in time series applications, forecasting can also be used in panel data due to its time dimension. However, there is limited number of studies in this area in the literature. In this thesis, forecasting is studied for panel data with binary response because of its increasing importance and increasing fundamental roles. A simulation study is held to compare the efficiency of different methods and to find the one that gives the optimal forecast values. In this simulation, 21 different methods, including na&iuml / ve and complex ones, are used by the help of R software. It is concluded that transition models and random effects models with no lag of response can be chosen for getting the most accurate forecasts, especially for the first two years of forecasting.

abstract: Longitudinal studies contain correlated data due to the repeated measurements on the same subject. The changing values of the time-dependent covariates and their association with the outcomes presents another source of correlation. Most methods used to analyze longitudinal data average the effects of time-dependent covariates on outcomes over time and provide a single regression coefficient per time-dependent covariate. This denies researchers the opportunity to follow the changing impact of time-dependent covariates on the outcomes. This dissertation addresses such issue through the use of partitioned regression coefficients in three different papers. In the first paper, an alternative approach to the partitioned Generalized Method of Moments logistic regression model for longitudinal binary outcomes is presented. This method relies on Bayes estimators and is utilized when the partitioned Generalized Method of Moments model provides numerically unstable estimates of the regression coefficients. It is used to model obesity status in the Add Health study and cognitive impairment diagnosis in the National Alzheimer’s Coordination Center database. The second paper develops a model that allows the joint modeling of two or more binary outcomes that provide an overall measure of a subject’s trait over time. The simultaneous modelling of all outcomes provides a complete picture of the overall measure of interest. This approach accounts for the correlation among and between the outcomes across time and the changing effects of time-dependent covariates on the outcomes. The model is used to analyze four outcomes measuring overall the quality of life in the Chinese Longitudinal Healthy Longevity Study. The third paper presents an approach that allows for estimation of cross-sectional and lagged effects of the covariates on the outcome as well as the feedback of the response on future covariates. This is done in two-parts, in part-1, the effects of time-dependent covariates on the outcomes are estimated, then, in part-2, the outcome influences on future values of the covariates are measured. These model parameters are obtained through a Generalized Method of Moments procedure that uses valid moment conditions between the outcome and the covariates. Child morbidity in the Philippines and obesity status in the Add Health data are analyzed. / Dissertation/Thesis / Doctoral Dissertation Statistics 2020

Orientador: Selene Maria Coelho Loibel / Resumo: Esta dissertação tem como foco a análise de dados categóricos, uma parte integrante da Análise Multivariada que interpreta a informação que está contida em dados discretos provenientes de contagens de eventos, possuindo características de nidas pela combinação das categorias de duas ou mais variáveis. A análise de dados categóricos é de grande importância dentro da Estatística pois tem aplicabilidade em variadas áreas do conhecimento. Os dados utilizados, foram coletados através de um question ário aplicado aos alunos de cinco Escolas Técnicas Estaduais (Etec) que nalizaram os cursos técnicos em 2018 e 2019. A pesquisa teve como objetivo obter dados locais e analisar se os alunos pretendem trabalhar ou continuar estudando na mesma área do curso que estão concluindo, se os alunos estão satisfeitos com os cursos que estão fazendo, se pretendem voltar para Etec e fazer outro curso complementar, entre outros questionamentos. Devido à natureza dos dados obtidos, as técnicas de análise de dados categóricos são adequadas e devem ser aplicadas para modelar e fazer inferências sobre os aspectos de interesse. Esta análise pode levar a resultados que serão de grande utilidade para essas Etecs. / Abstract: This dissertation focuses on the Categorical Data Analysis, an integral part of the Multivariate Analysis, which interprets embedded information in discrete data resulting from event counts, having characteristics de ned by combinations of categories from two or more variables. The categorical data analysis is of considerable importance within Statistics since it has a wide applicability in several areas of knowledge. The data set used was collected through a questionnaire applied to students from ve Public Technical Schools (Etec) that nished the technical courses in 2018 and 2019. The research aims to gather local data and analyze whether students intend to work or continue studying in the same eld of the technical course they are completing, whether students are satis ed with the courses they are attending, whether they want to go back to Etec and take another complementary course, among other questions. Due to the nature of the data obtained, categorized data analysis techniques are adequate and should be applied to model and make inferences about the aspects of interest. This analysis can be leaded to outcomes that will be very useful to these Etecs. / Mestre

Key sources of oil for western markets are located in complex geopolitical environments that increase economic and social risk. The amalgamation of economic, environmental, social and national security concerns for petroleum-based economies have created a renewed emphasis on alternative sources of energy which include biomass. The stability of sustainable biomass markets hinges on improved methods to predict and visualize business risk and cost to the supply chain. This thesis develops Bayesian logistic regression models, with comparisons of classical maximum likelihood models, to quantify significant factors that influence the siting of biomass-using facilities and predict potential locations in the 13-state Southeastern United States for three types of biomass-using facilities. Group I combines all biomass-using mills, biorefineries using agricultural residues and wood-using bioenergy/biofuels plants. Group II included pulp and paper mills, and biorefineries that use agricultural and wood residues. Group III included food processing mills and biorefineries that use agricultural and wood residues. The resolution of this research is the 5-digit ZIP Code Tabulation Area (ZCTA), and there are 9,416 ZCTAs in the 13-state Southeastern study region. For both classical and Bayesian approaches, a training set of data was used plus a separate validation (hold out) set of data using a pseudo-random number-generating function in SAS® Enterprise Miner. Four predefined priors are constructed. Bayesian estimation assuming a Gaussian prior distribution provides the highest correct classification rate of 86.40% for Group I; Bayesian methods assuming the non-informative uniform prior has the highest correct classification rate of 95.97% for Group II; and Bayesian methods assuming a Gaussian prior gives the highest correct classification rate of 92.67% for Group III. Given the comparative low sensitivity for Group II and Group III, a hybrid model that integrates classification trees and local Bayesian logistic regression was developed as part of this research to further improve the predictive power. The hybrid model increases the sensitivity of Group II from 58.54% to 64.40%, and improves both of the specificity and sensitivity significantly for Group III from 98.69% to 99.42% and 39.35% to 46.45%, respectively. Twenty-five optimal locations for the biomass-using facility groupings at the 5-digit ZCTA resolution, based upon the best fitted Bayesian logistic regression model and the hybrid model, are predicted and plotted for the 13-state Southeastern study region.

Financial failure is an important subject for both the economical development of the country and for the self - evaluation of individual companies. Increase in the number of financially failed companies points out the misuse of the country resources. Recently, financial failure threatens both small and large companies in Turkey. It is important to determine factors that affect the financial failure by analyzing models and to use these models for auditing the financial situation. In today&rsquo / s Turkey, the statistical methods that are used for this purpose involve single level models applied to cross-sectional data. However, multilevel models applied to panel data are more preferable as they gather more information, and also, enable the calculated financial success probabilities to be more trustworthy. In this thesis, publicly available panel data that are collected from The Istanbul Stock Exchange are investigated. Mainly, financial success of companies from two sectors, namely industry and services, are investigated. For the analysis of this panel data, data exploration methods, missing data imputation, possible solutions to multicollinearity problem, single level logistic regression models and multilevel models are used. By these models, financial success probabilities for each company are calculated / the factors related to the financial failure are determined, and changes in time are observed. Models and early warning systems resulted in correct classification rates of up to 100%. In the services sector, a small number of companies having publicly available data result in a decline in the success of models. It is concluded that sharing data with more subjects observed in a longer time period collected in the same format with academicians, will result in better justified outputs, which are useful for both academicians and managers.

Submitted by Viviane Lima da Cunha (viviane@biblioteca.ufpb.br) on 2017-07-06T13:03:15Z No. of bitstreams: 1 arquivototal.pdf: 2397887 bytes, checksum: 02b09da7c8ccdfb1dbbf31dd43c59ee8 (MD5) / Made available in DSpace on 2017-07-06T13:03:15Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2397887 bytes, checksum: 02b09da7c8ccdfb1dbbf31dd43c59ee8 (MD5) Previous issue date: 2017-02-23 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Heart diseases are responsible for more deaths in the first year of life than any other congenital problem in Brazil, affecting 8 to 10 children per 1000 live births. There are several types of heart diseases, some heal with time others require surgery. Evaluating the characteristics of the surgeries, it is possible to obtain the probability of the occurrence of postoperative complications and the estimation of the length of stay in the ICU (Intensive Care Unit) that varies according to the typology of this occurrence and the patient health condition. In this sense, the use of statistical models can help to optimize the care of patients in unfavorable clinical conditions. The aim of this study is to develop a tool based on statistical models to assist decision making about the chronological order of the surgeries to be performed. The data from this study came from the charts of the children destined to the execution of the surgery of congenital heart disease in the reference center that composes the Pediatric Cardiology Network PE-PB in the State of Paraíba. A logistic regression model was used to estimate the probability of occurrence of postoperative complications and survival analysis techniques to detect differences between the influence of determining factors on the length of ICU stay after the surgery. All data were analyzed in statistical software R, version 3.2.0. A total of 130 children were included, which 86.15% being below 10 years of age and weighing between 5 and 25 kg. Of the 72 children who presented post-surgical complications, 22.3% presented shunt-type cardiopathy, and 10% had Patent Ductus Arteriosus, followed by 9.2% with Tetralogy of Fallot. The risk factors identified by logistic regression as more associated with the outcome "developing post-surgical complications" were: high risk score (OR = 12.9; p-value = 0.02), presence of acyanotic obstructive heart disease (OR = 12.5, p-value = 0.006), the aortic clamping time during surgery greater than 20 minutes (OR = 3.3; p-value = 0.01), the time of extubation during the surgery (OR = 1.1, p-value = 0.07), presence of pulmonary arterial hypertension (OR = 6.7, p-value = 0.09) and age less than 6 months (OR = 3, 6; p-value = 0.05). In the survival analysis, it was possible to verify that there are statistically significant differences in length of ICU stay between children less than 6 months and older children; Also among children who presented high surgical risk and those who did not present; And among children where there is presence or absence of pulmonary arterial hypertension, in which the presence of some of these characteristics implies a greater probability of permanence for a certain time in the ICU. Also through the survival analysis, it was possible to observe that besides the factors identified through the logistic regression, the occurrence of postoperative infection in children also entails a longer hospitalization time after the surgery. Both techniques analyzed together, were able to build estimates for a certain hospital stay in cases of occurrence or not of postoperative complications, bringing support to hospital planning decisions, resulting in the optimization of the rotation of the available beds, in addition to the suggestion of chronological order of the queue of the next surgeries of congenital cardiopathy to be performed. / As cardiopatias são responsáveis por mais mortes no primeiro ano de vida do que qualquer outro problema congênito no Brasil, acometendo de 8 a 10 crianças a cada 1000 nascidos vivos. Existem diversos tipos de cardiopatia, algumas curam com o tempo, outras requerem intervenções cirúrgicas. Avaliando as características das cirurgias, é possível obter a probabilidade da ocorrência de complicações pós-cirúrgicas, e a estimativa do tempo de internamento em UTI que varia de acordo com a tipologia dessa ocorrência e com o perfil clínico do paciente. Neste sentido, a utilização de modelos estatísticos, pode auxiliar a otimização do cuidado a pacientes em condições clínicas desfavoráveis, sendo a proposta deste estudo, desenvolver uma ferramenta baseada em modelos estatísticos para auxiliar à tomada de decisões acerca da ordem cronológica das cirurgias a serem executadas. Os dados desse estudo provieram dos prontuários das crianças destinadas à execução da cirurgia de cardiopatia congênita no centro de referência que compõe a Rede de Cardiologia Pediátrica PE-PB no Estado da Paraíba. O modelo de regressão logística foi utilizado para estimar a probabilidade de ocorrência de complicações pós-cirúrgicas e as técnicas de análise de sobrevivência, para detectar diferenças entre a influência de fatores determinantes sobre os tempos de internamento em Unidades de Terapia Intensiva após a realização das cirurgias. Todos os dados foram analisados no software estatístico R, versão 3.2.0. Foram incluídas 130 crianças, sendo 86,15% com idade inferior a 10 anos de idade e peso se concentrando entre 5 e 25 quilos. Das 72 crianças que apresentaram complicações pós-cirúrgicas, 22,3% apresentaram a cardiopatia do tipo shunt, e no tocante ao diagnóstico, observou-se que 10% eram portadores de Persistência do Canal Arterial, seguido de 9,2% portadores de Tetralogia de Fallot. Os fatores de risco identificados pela regressão logística como mais associados com o desfecho “desenvolver complicações pós-cirúrgicas” foram: apresentar escore de risco alto (OR=12,9; p-valor=0,02), a presença de cardiopatia acianótica obstrutiva (OR=12,5; p-valor=0,006), o tempo de clampeamento aórtico durante a cirurgia ser superior a 20 minutos (OR=3,3; p-valor=0,01), o tempo de extubação durante a realização da cirurgia (OR=1,1; p-valor=0,07), a presença de hipertensão arterial pulmonar (OR=6,7; p-valor=0,09) e idade inferior a 6 meses (OR=3,6; p-valor=0,05). Na análise de sobrevivência, foi possível constatar que existem diferenças estatisticamente significativas sobre o tempo de internamento em UTI entre as crianças com menos de 6 meses de idade e as crianças com idade superior; também entre as crianças que apresentaram alto risco cirúrgico e as que não apresentaram; e entre as crianças onde há presença ou ausência de hipertensão arterial pulmonar, em que a presença de alguma(s) dessas características implica em maiores probabilidades de permanência por um determinado tempo em UTI. Ainda através da análise de sobrevivência, foi possível observar que além dos fatores identificados através da regressão logística, a ocorrência de infecção pós-operatória nas crianças também acarreta maior tempo de internamento após a cirurgia. Ambas as técnicas analisadas conjuntamente, foram capazes de construir estimativas para um determinado tempo de internamento hospitalar em casos de ocorrência ou não de complicações pós-cirúrgicas, trazendo apoio às decisões do planejamento hospitalar, resultando na otimização da rotatividade dos leitos disponíveis, além da sugestão de ordenação cronológica da fila de espera das próximas cirurgias de cardiopatia congênita a serem executadas.