Orientador: Douglas Silva Domingues / Resumo: O café é uma das commodities agrícolas tropicais mais comercializadas no mundo. Coffea arabica é a principal espécie utilizada para a produção comercial de café. A espécie é originária da Etiópia. Ela é única espécie alotetraploide do gênero (2n = 4x = 44) e se reproduz predominantemente por autofecundação. As cultivares comerciais de C. arabica possuem baixa diversidade genética, o que indica a necessidade de introgressão de alelos de germoplasma para o melhoramento dessas cultivares. Acessos do centro de origem da espécie possuem maior diversidade que as cultivares comerciais e podem ser utilizados para a identificação de novos alelos. O conteúdo de macronutrientes em grãos do cafeeiro tem impacto direto na qualidade do produto. No entanto, a base molecular da composição mineral de grãos de cafeeiro ainda é pouco conhecida. Com isso, o objetivo desse trabalho foi identificar marcadores SNP possivelmente associados com a composição de macronutrientes em grãos de C. arabica. Para alcance deste objetivo, foram comparados três métodos de imputação de genótipos, bem como foi realizado o mapeamento associativo em estudo de associação genômica ampla (GWAS). Foi utilizado um painel de 110 genótipos de C. arabica, composto por genótipos elite do programa de melhoramento do Instituto Agronômico do Paraná (3), cultivares comerciais (11) e acessos selvagens (96). Foram realizadas análises da composição de cinco macronutrientes (N, P, K, Ca e Mg) em grãos de cafeeiro coletados de 70 e 1... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Coffee is one of the most traded tropical commodities in the world. Coffea arabica is the main species used for commercial production. The species is originally from Ethiopia. In the Coffea genus, C. arabica is the only allotetraploid species (2n = 4x = 44) and it reproduces predominantly by self-fertilization. The commercial cultivars of C. arabica have a narrow genetic base that indicates the need for the introgression of new alleles from germplasm into coffee breeding programs. Wild accessions of C. arabica, from Ethiopia, have higher genetic diversity and can be used to identify new alleles. The macronutrient composition of the coffee grains has a direct impact on grain quality. However, the molecular basis for the mineral composition in coffee grains still poorly understood. Thus, the aim of this work was to perform mapping association analyses using the genome-wide association study (GWAS) technique to identify single nucleotide polymorphisms (SNPs) associated with macronutrient content in coffee grains from C. arabica. We also tested three imputation methods (haplotype missing allele imputation - Beagle, K-nearest neighbors, and Random Forest) in the genotypic data, and mapped it to two C. arabica reference genomes from the cultivar Caturra red and the spontaneous dihaploid Et39. We used a panel of 110 C. arabica genotypes, including elite landraces from the IAPAR coffee breeding program (3), commercial cultivars (11) and wild accessions (96). Analysis of the compositi... (Complete abstract click electronic access below) / Doutor

Missing data are a common problem in virtually all epidemiological research, especially when conducting longitudinal studies. In these settings, clinicians may collect biological samples to analyze changes in biomarkers, which often do not conform to parametric distributions and may be censored due to limits of detection. Using complete data from the BioCycle Study (2005-2007), which followed 259 premenopausal women over two menstrual cycles, we compared four techniques for handling missing biomarker data with non-Normal distributions. We imposed increasing degrees of missing data on two non-Normally distributed biomarkers under conditions of missing completely at random, missing at random, and missing not at random. Generalized estimating equations were used to obtain estimates from complete case analysis, multiple imputation using joint modeling, multiple imputation using chained equations, and multiple imputation using chained equations and predictive mean matching on Day 2, Day 13 and Day 14 of a standardized 28-day menstrual cycle. Estimates were compared against those obtained from analysis of the completely observed biomarker data. All techniques performed comparably when applied to a Normally distributed biomarker. Multiple imputation using joint modeling and multiple imputation using chained equations produced similar estimates across all types and degrees of missingness for each biomarker. Multiple imputation using chained equations and predictive mean matching consistently deviated from both the complete data estimates and the other missing data techniques when applied to a biomarker with a bimodal distribution. When addressing missing biomarker data in longitudinal studies, special attention should be given to the underlying distribution of the missing variable. As biomarkers become increasingly Normal, the amount of missing data tolerable while still obtaining accurate estimates may also increase when data are missing at random. Future studies are necessary to assess these techniques under more elaborate missingness mechanisms and to explore interactions between biomarkers for improved imputation models.

No description available.

The present research studies marital dissolution using data from both spouses from the National Survey of Families and Households (NSFH) and uses the method of multiple imputation to handle missing data. Role theory and another four approaches (social exchange theory, stake theory, gender perspective and heterogeneity perspective) are used to make a methodological argument why using data from both spouses is necessary to study marital stability. Five data sets are imputed and there are 3,777 observations in each imputed data set. Main research findings are as followed. First, the model fits of the data from both spouses on marital dissolution are significantly better than the model fits of the data from one spouse only; therefore, gathering perceptual data from both spouses is necessary to understand marital dissolution. Second, overall, the effects of most spousal discrepancies do not support the heterogeneity perspective. Third, the model fits of the wife only model are significantly better than the model fits of the husband only model across different periods of marital duration, and the predictability of wives' variables is more stable than husbands' variables. Therefore, if only individual-level data are available to use, researchers are encouraged to use wives' data rather than husbands' data. Fourth, the predictability of factors varies with marital duration and gender in the models with data from both spouses.

No description available.

La presente investigación se enfocó en determinar cómo la aplicación mixta del injusto organizacional y personal en la criminalidad organizada contribuiría a imputar a sus miembros inactivos, por lo que se abordaron discordancias normativas respecto a la sanción en casos de organizaciones criminales, las cuales aplican sanciones tanto a nivel organizacional como personal. Asimismo, el estudio se centró en desarrollar la figura de imputación subjetiva sistémica, con el objetivo de aplicar de manera conjunta el injusto organizacional y el injusto personal en delitos de criminalidad organizada, con el propósito de prevenir la impunidad de los miembros inactivos que contribuyen al funcionamiento delictivo de la organización. Finalmente se aplicó un enfoque metodológico cualitativo para lograr estos objetivos, permitiendo una comprensión más profunda de las complejidades involucradas en la imputación de este tipo de delitos. / The present investigation focused on determining how the mixed application of organizational and personal injustice in organized crime would contribute to charging its inactive members, so normative discrepancies were addressed regarding the sanction in cases of criminal organizations, which apply sanctions both at an organizational and personal level. Likewise, the study focused on developing the figure of systemic subjective imputation, with the objective of jointly applying organizational injustice and personal injustice in organized crime crimes, with the purpose of preventing impunity for inactive members who contribute to the crime. criminal operation of the organization. Finally, a qualitative methodological approach was applied to achieve these objectives, allowing a deeper understanding of the complexities involved in the imputation of this type of crimes.

<p dir="ltr">The last decade has witnessed an unprecedented rise in the application of machine learning in high-stake automated decision-making systems such as hiring, policing, bail sentencing, medical screening, etc. The long-lasting impact of these intelligent systems on human life has drawn attention to their fairness implications. A majority of subsequent studies targeted the existing historically unfair decision labels in the training data as the primary source of bias and strived toward either removing them from the dataset (de-biasing) or avoiding learning discriminatory patterns from them during training. In this thesis, we show label bias is not a necessary condition for unfair outcomes from a machine learning model. We develop theoretical and empirical evidence showing that biased model outcomes can be introduced by a range of different data properties and components of the machine learning development pipeline.</p><p dir="ltr">In this thesis, we first prove that machine learning models are expected to introduce bias even when the training data doesn’t include label bias. We use the proof-by-construction technique in our formal analysis. We demonstrate that machine learning models, trained to optimize for joint accuracy, introduce bias even when the underlying training data is free from label bias but might include other forms of disparity. We identify two data properties that led to the introduction of bias in machine learning. They are the group-wise disparity in the feature predictivity and the group-wise disparity in the rates of missing values. The experimental results suggest that a wide range of classifiers trained on synthetic or real-world datasets are prone to introducing bias under feature disparity and missing value disparity independently from or in conjunction with the label bias. We further analyze the trade-off between fairness and established techniques to improve the generalization of machine learning models such as adversarial training, increasing model complexity, etc. We report that adversarial training sacrifices fairness to achieve robustness against noisy (typically adversarial) samples. We propose a fair re-weighted adversarial training method to improve the fairness of the adversarially trained models while sacrificing minimal adversarial robustness. Finally, we observe that although increasing model complexity typically improves generalization accuracy, it doesn’t linearly improve the disparities in the prediction rates.</p><p dir="ltr">This thesis unveils a vital limitation of machine learning that has yet to receive significant attention in FairML literature. Conventional FairML literature reduces the ML fairness task to as simple as de-biasing or avoiding learning discriminatory patterns. However, the reality is far away from it. Starting from deciding on which features collect up to algorithmic choices such as optimizing robustness can act as a source of bias in model predictions. It calls for detailed investigations on the fairness implications of machine learning development practices. In addition, identifying sources of bias can facilitate pre-deployment fairness audits of machine learning driven automated decision-making systems.</p>

L'imputation est souvent utilisée dans les enquêtes pour traiter la non-réponse partielle. Il est bien connu que traiter les valeurs imputées comme des valeurs observées entraîne une sous-estimation importante de la variance des estimateurs ponctuels. Pour remédier à ce problème, plusieurs méthodes d'estimation de la variance ont été proposées dans la littérature, dont des méthodes adaptées de rééchantillonnage telles que le Bootstrap et le Jackknife. Nous définissons le concept de double-robustesse pour l'estimation ponctuelle et de variance sous l'approche par modèle de non-réponse et l'approche par modèle d'imputation. Nous mettons l'emphase sur l'estimation de la variance à l'aide du Jackknife qui est souvent utilisé dans la pratique. Nous étudions les propriétés de différents estimateurs de la variance à l'aide du Jackknife pour l'imputation par la régression déterministe ainsi qu'aléatoire. Nous nous penchons d'abord sur le cas de l'échantillon aléatoire simple. Les cas de l'échantillonnage stratifié et à probabilités inégales seront aussi étudiés. Une étude de simulation compare plusieurs méthodes d'estimation de variance à l'aide du Jackknife en terme de biais et de stabilité relative quand la fraction de sondage n'est pas négligeable. Finalement, nous établissons la normalité asymptotique des estimateurs imputés pour l'imputation par régression déterministe et aléatoire. / Imputation is often used in surveys to treat item nonresponse. It is well known that treating the imputed values as observed values may lead to substantial underestimation of the variance of the point estimators. To overcome the problem, a number of variance estimation methods have been proposed in the literature, including appropriate versions of resampling methods such as the jackknife and the bootstrap. We define the concept of doubly robust point and variance estimation under the so-called nonresponse and imputation model approaches. We focus on jackknife variance estimation, which is widely used in practice. We study the properties of several jackknife variance estimators under both deterministic and random regression imputation. We first consider the case of simple random sampling without replacement. The case of stratified simple random sampling and unequal probability sampling is also considered. A limited simulation study compares various jackknife variance estimators in terms of bias and relative stability when the sampling fraction is not negligible. Finally, the asymptotic normality of imputed estimator is established under both deterministic and random regression imputation.

L'imputation est souvent utilisée dans les enquêtes pour traiter la non-réponse partielle. Il est bien connu que traiter les valeurs imputées comme des valeurs observées entraîne une sous-estimation importante de la variance des estimateurs ponctuels. Pour remédier à ce problème, plusieurs méthodes d'estimation de la variance ont été proposées dans la littérature, dont des méthodes adaptées de rééchantillonnage telles que le Bootstrap et le Jackknife. Nous définissons le concept de double-robustesse pour l'estimation ponctuelle et de variance sous l'approche par modèle de non-réponse et l'approche par modèle d'imputation. Nous mettons l'emphase sur l'estimation de la variance à l'aide du Jackknife qui est souvent utilisé dans la pratique. Nous étudions les propriétés de différents estimateurs de la variance à l'aide du Jackknife pour l'imputation par la régression déterministe ainsi qu'aléatoire. Nous nous penchons d'abord sur le cas de l'échantillon aléatoire simple. Les cas de l'échantillonnage stratifié et à probabilités inégales seront aussi étudiés. Une étude de simulation compare plusieurs méthodes d'estimation de variance à l'aide du Jackknife en terme de biais et de stabilité relative quand la fraction de sondage n'est pas négligeable. Finalement, nous établissons la normalité asymptotique des estimateurs imputés pour l'imputation par régression déterministe et aléatoire. / Imputation is often used in surveys to treat item nonresponse. It is well known that treating the imputed values as observed values may lead to substantial underestimation of the variance of the point estimators. To overcome the problem, a number of variance estimation methods have been proposed in the literature, including appropriate versions of resampling methods such as the jackknife and the bootstrap. We define the concept of doubly robust point and variance estimation under the so-called nonresponse and imputation model approaches. We focus on jackknife variance estimation, which is widely used in practice. We study the properties of several jackknife variance estimators under both deterministic and random regression imputation. We first consider the case of simple random sampling without replacement. The case of stratified simple random sampling and unequal probability sampling is also considered. A limited simulation study compares various jackknife variance estimators in terms of bias and relative stability when the sampling fraction is not negligible. Finally, the asymptotic normality of imputed estimator is established under both deterministic and random regression imputation.

資料在企業資訊來源中扮演了極為重要的角色，特別是在現今知識與技術的世代裡。如果對於一個有意義且具有代表性資料庫中的遺漏值能夠正確的處理，那麼對於企業資訊而言，是一個大有可為的突破。 然而，有時我們或許會遇到一些不是那麼完善的資料庫，當資料庫中的資料有遺漏值時，從這樣資料庫中所獲得的結果，或許會是一些有偏差或容易令人誤解的結果。因此，本研究的目的在於插補遺漏值為資料庫加值，進而根據遺漏值類型建立插補模型。 如果遺漏值為連續型，用迴歸模型和倒傳遞類神經模型來進行插補；如果遺漏值為類別型，採用邏輯斯迴歸、倒傳遞類神經和決策樹進行插補分析。經由模擬的結果顯示，對於連續型的遺漏值，迴歸模型提供了最佳的插補估計；而類別型的遺漏值，C5.0決策樹是最佳的選擇。此外，對於資料庫中的稀少資料，當連續型的遺漏值，倒傳遞類神經模型提供了最佳的插補估計；而類別型的遺漏值，亦是C5.0決策樹是最佳的選擇。 / Data plays a vital role as source of information to the organization especially in the era of information and technology. A meaningful, qualitative and representative database if properly handled could mean a promising breakthrough to the organizations. However, from time to time, we may encounter a not so perfect database, that is we have the situation where the data in the database is missing. With the incomplete database, the results obtained from such database may provide biased or misleading solutions. Therefore, the purpose of this research is to place its emphasis on imputing missing data of the value-added database then builds the model in accordance to the type of data. If the missing data type is continuous, regression model and BPNN neural network is applied. If the missing data type is categorical, logistic regression, BPNN neural network and decision tree is chosen for the application. Our result has shown that for the continuous missing data, the regression model proved to deliver the best estimate. For the categorical missing data, C5.0 decision tree model is the chosen one. Besides, as regards the rare data missing in the database, our result has shown that for the continuous missing data, the BPNN neural network proved to deliver the best estimate. For the categorical missing data, C5.0 decision tree model is the chosen one.